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      "                <span class=\"img-span\"><img src=\"//k.sinaimg.cn/n/news/1_img/upload/3aa4af12/664/w899h565/20200129/2085-intiarn9469705.jpg/w500h333l80524.jpg\" width=\"500\" height=\"333\" alt=\"货机满载医疗防疫物资抵达武汉\" class=\"img-hover\" /></span>\n",
      "                <span class=\"title\">货机满载医疗防疫物资抵达武汉</span>\n",
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      "                <a href=\"http://english.sina.com/\" target=\"_blank\">SINA English</a>\n",
      "            </li>\n",
      "        </ul>\n",
      "\n",
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      "    <div class=\"report-infor\">\n",
      "        <p>违法和不良信息举报电话：4000520066<br/>\n",
      "        举报邮箱：jubao@vip.sina.com</p>\n",
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      "        <p>All Rights Reserved  新浪公司 <a href=\"https://corp.sina.com.cn/chn/copyright.html\" target=\"_blank\">版权所有</a> </p>\n",
      "    </div>\n",
      "</div>\n",
      "        </div>\n",
      "    </div>\n",
      "    <!-- 正文页浮层 -->\n",
      "<div class=\"page-right-bar\">\n",
      "    <span class=\"fbr-box fbr-down\">\n",
      "        <span class=\"fbr-pic\" id=\"weixinPic\">\n",
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      "\n",
      "            <img src=\"//n.sinaimg.cn/tech/index_v17/xinwen_fix.png\" class=\"weixin_big\">\n",
      "\n",
      "            <span class=\"fbr-title\" id=\"weixinTitle\">下载<i>新浪新闻</i></span>\n",
      "        </span>\n",
      "\n",
      "    </span>\n",
      "    <a class=\"right-bar-btn fbr-pic\" id=\"weixinText\" href=\"javascript:void(0);\" data-sudaclick=\"float_newsapp_i\">\n",
      "        新浪新闻App\n",
      "    </a>\n",
      "    <img src=\"//n.sinaimg.cn/tech/index_v17/xinwen_fix.png\" class=\"weixin_big1\">\n",
      "    <!-- <a href=\"javascript\" data-sudaclick=\"float_home_p\" node-type=\"toHome\" class='right-bar-btn btn-home'>>新浪新闻客户端</a> -->\n",
      "    <a href=\"http://news.sina.com.cn/feedback/post.html\" target=\"_blank\" data-sudaclick=\"float_feedback_p\" class=\"right-bar-btn\">\n",
      "        <div class=\" btn-survey\"></div>\n",
      "        <p>意见反馈</p>\n",
      "    </a>\n",
      "   <a data-sudaclick=\"float_survey_p\" node-type=\"sittingA\" class=\"right-bar-btn sittingA\"></a>\n",
      "    <a href=\"javascript:;\" data-sudaclick=\"float_top_i\" node-type=\"toTop\" class='right-bar-btn fadeOut'>\n",
      "        <div class=\"btn-toTop\"></div>\n",
      "        <p>返回顶部</p>\n",
      "    </a>\n",
      "</div>\n",
      "    <!--对联广告 20150909 15:10:00 leitao Start-->\n",
      "    <ins class=\"sinaads\" data-ad-pdps=\"PDPS000000058097\" data-ad-type=\"float\"></ins>\n",
      "    <script>(sinaads = window.sinaads || []).push({\n",
      "        params : {\n",
      "            sinaads_float_show_pos: 800,\n",
      "            sinaads_float_top : 46\n",
      "        }\n",
      "    });\n",
      "    </script>\n",
      "    <!--对联广告 20150909 15:10:00 leitao End-->\n",
      "    <!-- start dmp -->\n",
      "    <script type=\"text/javascript\">\n",
      "    (function(d, s, id) {\n",
      "    var n = d.getElementsByTagName(s)[0];\n",
      "    if (d.getElementById(id)) return;\n",
      "    s = d.createElement(s);\n",
      "    s.id = id;\n",
      "    s.setAttribute('charset', 'utf-8');\n",
      "    s.src = '//d' + Math.floor(0 + Math.random() * (8 - 0 + 1)) + '.sina.com.cn/litong/zhitou/sinaads/src/spec/sinaads_ck.js';\n",
      "    n.parentNode.insertBefore(s, n);\n",
      "    })(document, 'script', 'sinaads-ck-script');\n",
      "    </script>\n",
      "    <!-- end dmp -->\n",
      "    <script type=\"text/javascript\" src=\"//tech.sina.com.cn/js/717/20140827/index2014/top.js\" charset=\"gb2312\"></script>\n",
      "    <script language='javascript' charset='utf-8' src='//i.sso.sina.com.cn/js/ssologin.js'></script>\n",
      "    <script type=\"text/javascript\" src=\"//i.sso.sina.com.cn/js/outlogin_layer.js\" charset=\"utf-8\"></script>\n",
      "    <script type=\"text/javascript\" src=\"//i.sso.sina.com.cn/js/user_panel_new_version_v2.js\" charset=\"utf-8\"></script>\n",
      "    <script type=\"text/javascript\" src=\"//ent.sina.com.cn/470/2014/0328/search_suggest.js\"></script>\n",
      "    <script charset=\"utf-8\" type=\"text/javascript\" src=\"//n.sinaimg.cn/common/channelnav/js/nav.js\"></script>\n",
      "    <!-- FEED start -->\n",
      "\n",
      "    <!--feed流配置-->\n",
      "       <script type=\"text/javascript\">\n",
      "        var FEED_CARD_INFO = {\n",
      "            containerID: 'feed_cont', // 容器ID\n",
      "            containerWidth: 672, // 默认宽度是660\n",
      "            loadType: 1, // 加载类型 1表示点击更多加载, 2表示自动加载\n",
      "            autoLoadDistance: '', // 如果是自动加载, 有时候feed流并没有放在最下面, 所以scrollEnd的时候要判断这个距离\n",
      "            clickLoadText: '点击加载更多',\n",
      "            summaryKey: 'summary', // 摘要使用字段\n",
      "            supportNotification: true,// 最左边的列表是否添加提醒功能\n",
      "            notificationTemplate: '有{n}条消息，点击查看',// 提醒的格式\n",
      "            notificationTimeout: 1000 * 30,// 提醒的事件间隔\n",
      "            pageSize: 20, // 每次加载条数\n",
      "            stepSize: 1, // 加载三次之后出分页\n",
      "            moreTagTemplate: '点击查看更多关于“{n}”的文章',\n",
      "\n",
      "            /*\n",
      "             * 评论相关配置\n",
      "             */\n",
      "            // 1. 是否支持当前页展开评论\n",
      "            enablePageComment: false,\n",
      "            // 2. 评论组件编码,默认是gbk\n",
      "            pageCommentEncoding: 'gbk',\n",
      "            // 3. 是否显示盖楼,默认0,还可以为1\n",
      "            pageCommentShowReply: 0,\n",
      "            // 4. 评论显示字数\n",
      "            pageCommentMaxWordCount: 110,\n",
      "            // 5. 最热评论默认显示条数\n",
      "            pageCommentHotPageNum: 3,\n",
      "            // 6. 最新评论默认显示条数\n",
      "            pageCommentFirstPageNum: 10,\n",
      "\n",
      "            pageid: 121, // pageid与lid共同定位一个api地址\n",
      "            homeid: 30003, // 用来上传定制信息, 见邮件[feed流订阅homeid分配]\n",
      "\n",
      "            supportTabEdit: false, //是否支持兴趣设置\n",
      "            editTips: ['温馨提示：点击下一步，调整分类顺序。（至少选择一个分类）', '温馨提示：完成设置后，将会根据兴趣推荐全新内容~'],\n",
      "            toFilterNews: [], // 过滤从feed系统走，此处可置空\n",
      "            isTabFixed: false,\n",
      "            fixedTop: 0,\n",
      "            supportKeywords: false, // 是否支持热词定制\n",
      "            hotNewsCount: 200, //评论超过该数字显示热icon\n",
      "            enableTagFeed: false,// 是否支持点击关键词加载列表，默认是false\n",
      "            tagURL: 'http://tags.finance.sina.com.cn',\n",
      "            yPosition: 'auto', // 如果不固定，则给一个auto，自动计算\n",
      "            tabHeight: 50,\n",
      "            noDataText: '没有相关新闻',\n",
      "            //最左边固定tab\n",
      "            firstTab: {\n",
      "                txt: '最新新闻',\n",
      "                lid: 1356\n",
      "            },\n",
      "\n",
      "            // 默认tab\n",
      "            tabs: [-4000],\n",
      "            allTabs: [-4000],\n",
      "            tabInfos: {\n",
      "/*\n",
      "                'tab_-4000': {\n",
      "                    txt: '理财自媒体',\n",
      "                    lid: -4000,\n",
      "                    //url: 'http://feed.mix.sina.com.cn/api/roll/get?pageid=155&lid=1686&num=10&page=1'\n",
      "                    //url: 'http://feed.mix.sina.com.cn/api/zt/get?sid_1_value=2016qglh&sid_2_value=3&channelid=1&len=5'\n",
      "                    url: 'http://cj.sina.com.cn/api/article/feed_author?num=10&page=1'\n",
      "                }\n",
      "*/\n",
      "            },\n",
      "            requireLogin: function(){\n",
      "              var UserPanel = SINA_USER_PANEL;\n",
      "              UserPanel.setOutLoginMiddle();\n",
      "              UserPanel.getOutLogin().show();\n",
      "              // 可添加提示\n",
      "              __SinaTopBar__.user.showTip('<span>温馨提示: </span>您已经完成兴趣设置，建议登录后保存。');\n",
      "            },\n",
      "            isLogin: function(){\n",
      "              if(window.SINA_OUTLOGIN_LAYER){\n",
      "                return window.SINA_OUTLOGIN_LAYER.isLogin();\n",
      "              }\n",
      "              return false;\n",
      "            }\n",
      "        };\n",
      "    </script>\n",
      "    <script type=\"text/javascript\" charset=\"gb2312\" src=\"//tech.sina.com.cn/js/717/20141112/index2014/doT.min.js\"></script>\n",
      "    <script type=\"text/javascript\" charset=\"gb2312\" src=\"//news.sina.com.cn/blank/tianyi/feed-news.js\"></script>\n",
      "    <!--/feed流配置-->\n",
      "\t<script type=\"text/javascript\" src=\"//n.sinaimg.cn/news/643213b9/20180725/suda_log_1459340870608.js\"></script> \n",
      "    <script type=\"text/javascript\" src=\"//www.sinaimg.cn/unipro/pub/suda_m_v634.js\"></script>\n",
      "    <script type=\"text/javascript\">suds_init(979,100.0000,1515,2)</script>\n",
      "<script type=\"text/javascript\" src=\"//simg.sinajs.cn/products/news_finance/2018/pc_china_home/js/vender-5b1f99.js\"></script><script type=\"text/javascript\" src=\"//simg.sinajs.cn/products/news_finance/2018/pc_china_home/js/index-2b1eed.js\"></script><!-- body code begin -->\n",
      "<script type=\"text/javascript\">\n",
      "(function(){\n",
      "    if(window.top !== window.self || window._thereIsNoRealTimeMessage){return};\n",
      "    var script = document.createElement('script');\n",
      "    script.setAttribute('charset', 'gb2312');\n",
      "    script.src = '//news.sina.com.cn/js/694/2012/0830/realtime.js?ver=1.5.1';\n",
      "    document.getElementsByTagName('head')[0].appendChild(script);\n",
      "})();\n",
      "</script>\n",
      "\n",
      "<!-- SSO_UPDATECOOKIE_START -->\n",
      "<script type=\"text/javascript\">var sinaSSOManager=sinaSSOManager||{};sinaSSOManager.q=function(b){if(typeof b!=\"object\"){return\"\"}var a=new Array();for(key in b){a.push(key+\"=\"+encodeURIComponent(b[key]))}return a.join(\"&\")};sinaSSOManager.es=function(f,d,e){var c=document.getElementsByTagName(\"head\")[0];var a=document.getElementById(f);if(a){c.removeChild(a)}var b=document.createElement(\"script\");if(e){b.charset=e}else{b.charset=\"gb2312\"}b.id=f;b.type=\"text/javascript\";d+=(/\\?/.test(d)?\"&\":\"?\")+\"_=\"+(new Date()).getTime();b.src=d;c.appendChild(b)};sinaSSOManager.doCrossDomainCallBack=function(a){sinaSSOManager.crossDomainCounter++;document.getElementsByTagName(\"head\")[0].removeChild(document.getElementById(a.scriptId))};sinaSSOManager.crossDomainCallBack=function(a){if(!a||a.retcode!=0){return false}var d=a.arrURL;var b,f;var e={callback:\"sinaSSOManager.doCrossDomainCallBack\"};sinaSSOManager.crossDomainCounter=0;if(d.length==0){return true}for(var c=0;c<d.length;c++){b=d[c];f=\"ssoscript\"+c;e.scriptId=f;b=b+(/\\?/.test(b)?\"&\":\"?\")+sinaSSOManager.q(e);sinaSSOManager.es(f,b)}};sinaSSOManager.updateCookieCallBack=function(c){var d=\"ssoCrossDomainScriptId\";var a=\"http://login.sina.com.cn/sso/crossdomain.php\";if(c.retcode==0){var e={scriptId:d,callback:\"sinaSSOManager.crossDomainCallBack\",action:\"login\",domain:\"sina.com.cn\"};var b=a+\"?\"+sinaSSOManager.q(e);sinaSSOManager.es(d,b)}else{}};sinaSSOManager.updateCookie=function(){var g=1800;var p=7200;var b=\"ssoLoginScript\";var h=3600*24;var i=\"sina.com.cn\";var m=1800;var l=\"http://login.sina.com.cn/sso/updatetgt.php\";var n=null;var f=function(e){var r=null;var q=null;switch(e){case\"sina.com.cn\":q=sinaSSOManager.getSinaCookie();if(q){r=q.et}break;case\"sina.cn\":q=sinaSSOManager.getSinaCookie();if(q){r=q.et}break;case\"51uc.com\":q=sinaSSOManager.getSinaCookie();if(q){r=q.et}break}return r};var j=function(){try{return f(i)}catch(e){return null}};try{if(g>5){if(n!=null){clearTimeout(n)}n=setTimeout(\"sinaSSOManager.updateCookie()\",g*1000)}var d=j();var c=(new Date()).getTime()/1000;var o={};if(d==null){o={retcode:6102}}else{if(d<c){o={retcode:6203}}else{if(d-h+m>c){o={retcode:6110}}else{if(d-c>p){o={retcode:6111}}}}}if(o.retcode!==undefined){return false}var a=l+\"?callback=sinaSSOManager.updateCookieCallBack\";sinaSSOManager.es(b,a)}catch(k){}return true};sinaSSOManager.updateCookie();</script>\n",
      "<!-- SSO_UPDATECOOKIE_END -->\n",
      "\n",
      "<!-- body code end -->\n",
      "\n",
      "</body>\n",
      "\n",
      "</html>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "res=requests.get('http://news.sina.com.cn/china/')\n",
    "res.encoding='utf-8'\n",
    "print(res.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   \"hello world\" This is link1 This is link2  \n"
     ]
    }
   ],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "html_sample = ' \\\n",
    "<html>\\\n",
    " <body>\\\n",
    " <h1 id=title>\"hello world</h1>\"\\\n",
    " <a href=\"#\" class=\"link\">This is link1</a> \\\n",
    " <a href=\"# link2\" class=\"link\">This is link2</a> \\\n",
    "</body> \\\n",
    "</html>'\n",
    "soup=BeautifulSoup(html_sample,'html.parser')\n",
    "print(soup.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<h1 id=\"title\">\"hello world</h1>]\n",
      "\"hello world\n"
     ]
    }
   ],
   "source": [
    "header=soup.select('h1')\n",
    "print(header)\n",
    "print(header[0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<a class=\"link\" href=\"#\">This is link1</a>, <a class=\"link\" href=\"# link2\">This is link2</a>]\n",
      "This is link2\n",
      "This is link1\n",
      "This is link2\n"
     ]
    }
   ],
   "source": [
    "header1=soup.select('a')\n",
    "print(header1)\n",
    "print(header1[1].text)\n",
    "for link in header1:\n",
    "    print(link.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<h1 id=\"title\">\"hello world</h1>]\n",
      "<a class=\"link\" href=\"#\">This is link1</a>\n",
      "<a class=\"link\" href=\"# link2\">This is link2</a>\n"
     ]
    }
   ],
   "source": [
    "title=soup.select('#title')\n",
    "link=soup.select('.link')\n",
    "print(title)\n",
    "for link1 in link:\n",
    "    print(link1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<a class=\"link\" href=\"#\">This is link1</a>\n",
      "#\n",
      "<a class=\"link\" href=\"# link2\">This is link2</a>\n",
      "# link2\n"
     ]
    }
   ],
   "source": [
    "alinks=soup.select('a')\n",
    "for link in  alinks:\n",
    "    print(link)\n",
    "    print(link['href'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "res=requests.get('http://news.sina.com.cn/china/')\n",
    "res.encoding='utf-8'\n",
    "soup=BeautifulSoup(res.text,'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2月19日 19:18 揭秘中国第一代核潜艇:4万多个零件全部是国产 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrrhct9700504.shtml\n",
      "2月19日 18:20 北京牌照小微客车22日起在津环内早晚高峰不限行 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrswmu2728771.shtml\n",
      "2月19日 18:16 湖南祁阳被刺伤村支书是省劳模 担任该职务已28年 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrswmu2721005.shtml\n",
      "2月19日 17:34 四川雅江森林火灾系人为失火 嫌疑人已被控制(图) http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189473.shtml\n",
      "2月19日 17:34 大连鞍子山积石冢考古发掘收官 清理墓室133座 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189438.shtml\n",
      "2月19日 17:29 节后返京高峰明日正式来临 地铁将视客流加开临客 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrswmu2628511.shtml\n",
      "2月19日 16:33 新京报斥自媒体“点赞”杀人嫌犯张扣扣:混淆是非 http://news.sina.com.cn/c/zs/2018-02-19/doc-ifyrrmye2724827.shtml\n",
      "2月19日 16:12 外交部:刘延东将赴韩国出席平昌冬奥会闭幕式 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8164691.shtml\n",
      "2月19日 15:50 中央气象台：多地明起大风降温 南方阴雨天气持续 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8156251.shtml\n",
      "2月19日 15:07 纪律处分后执行得如何?北京开展检查形成问责闭环 http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9624041.shtml\n",
      "2月19日 14:00 航行自由≠军事行动自由 中方驳斥对南海问题误读 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrqwkc8120528.shtml\n",
      "2月19日 13:08 中国驻巴拿马大使：中巴“每天都在创造历史” http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9584633.shtml\n",
      "2月19日 12:49 四川雅江森林火灾1500多人灭火 过火面积已66公顷 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrrmye2680732.shtml\n",
      "2月19日 12:17 或非明画 探春晚亮相的《丝路山水地图》更名背后 http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9567437.shtml\n",
      "2月19日 17:34 四川雅江森林火灾系人为失火 嫌疑人已被控制(图) http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189473.shtml\n",
      "2月19日 17:34 大连鞍子山积石冢考古发掘收官 清理墓室133座 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189438.shtml\n",
      "2月19日 17:29 节后返京高峰明日正式来临 地铁将视客流加开临客 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrswmu2628511.shtml\n",
      "2月19日 16:43 傅莹战斗力再次爆表 见招拆招驳斥“中国威胁论” http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrswmu2547688.shtml\n",
      "2月19日 16:12 外交部:刘延东将赴韩国出席平昌冬奥会闭幕式 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8164691.shtml\n",
      "2月19日 15:50 中央气象台：多地明起大风降温 南方阴雨天气持续 http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8156251.shtml\n",
      "2月19日 15:07 纪律处分后执行得如何?北京开展检查形成问责闭环 http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9624041.shtml\n",
      "2月19日 14:55 美国叫嚣继续巡航南海 称中国在永暑礁建通信中心 http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9620806.shtml\n",
      "2月19日 14:00 航行自由≠军事行动自由 中方驳斥对南海问题误读 http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrqwkc8120528.shtml\n",
      "2月19日 13:08 中国驻巴拿马大使：中巴“每天都在创造历史” http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9584633.shtml\n",
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      "2月19日 12:29 中国第5个南极科考站正准备 位于西南极罗斯海 http://news.sina.com.cn/o/2018-02-19/doc-ifyrrhct9571824.shtml\n",
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      "2月16日 08:57 人民网评：春晚亮出“文化自信”这张王牌 http://news.sina.com.cn/c/zs/2018-02-16/doc-ifyrrmye2062428.shtml\n",
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      "2月14日 13:36 海外网:中国共产党这场“自我革命”永远不停歇 http://news.sina.com.cn/c/zs/2018-02-14/doc-ifyrpeie5318678.shtml\n",
      "2月14日 10:35 中国冬奥队4人被罚出局 媒体:冰场冷但我们血很热 http://news.sina.com.cn/o/2018-02-14/doc-ifyrqwkc6252493.shtml\n",
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      "2月14日 07:55 中纪委机关刊：相对于老虎 群众对蝇贪感受更真切 http://news.sina.com.cn/c/zs/2018-02-14/doc-ifyrkzqr3289137.shtml\n",
      "2月14日 06:45 法制日报评直播乱象：直播平台不是任性的名利场 http://news.sina.com.cn/c/zs/2018-02-14/doc-ifyrpeie4309904.shtml\n",
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      "2月13日 18:16 曾繁新被查用监委改革后新提法:涉嫌严重职务违法 http://news.sina.com.cn/c/2018-02-13/doc-ifyrkzqr3088086.shtml\n",
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      "2月12日 20:21 北大原副校长罗豪才逝世 常年骑破旧自行车 http://news.sina.com.cn/c/zs/2018-02-12/doc-ifyrmfmc2067823.shtml\n",
      "2月12日 19:36 中纪委前后脚处分两个央管干部 巧的是都叫刘君 http://news.sina.com.cn/c/zs/2018-02-12/doc-ifyrmfmc2058542.shtml\n",
      "2月12日 16:37 浙江最年轻市委书记仕途有变 蔡奇评价他处事老练 http://news.sina.com.cn/c/zs/2018-02-12/doc-ifyrmfmc2021015.shtml\n",
      "2月12日 04:35 军报谈军队反腐：军中绝不能有腐败分子藏身之地 http://news.sina.com.cn/o/2018-02-12/doc-ifyrkzqr2201694.shtml\n",
      "2月11日 20:44 新京报评代小权无罪:用法治正义托起企业家的信心 http://news.sina.com.cn/c/2018-02-11/doc-ifyrkrva7481961.shtml\n",
      "2月5日 17:49 陕西千亿矿权纠纷:“黑金”争夺战背后的权力寻租 http://news.sina.com.cn/o/2018-02-05/doc-ifyreuzn3274822.shtml\n",
      "1月31日 13:14 烟草提税带来控烟红利2年吃空：去年销量止跌反升 http://news.sina.com.cn/c/sd/2018-01-31/doc-ifyrcsrw0579916.shtml\n",
      "1月29日 05:08 孩子先会打游戏再会系鞋带 如何智斗他们手机瘾？ http://news.sina.com.cn/o/2018-01-29/doc-ifyqyesy3391624.shtml\n",
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      "1月27日 11:38 儿童邪典片流入中国毒害儿童 邪典文化到底是什么 http://news.sina.com.cn/o/2018-01-27/doc-ifyqyqni3704922.shtml\n",
      "1月26日 07:50 聚焦临终关怀：没有经济收益 医院普遍缺乏动力 http://news.sina.com.cn/c/sd/2018-01-26/doc-ifyqwiqk8352258.shtml\n",
      "1月25日 15:03 山东原副省长季缃琦跌落“银座”:被举报侵吞国资 http://news.sina.com.cn/c/sd/2018-01-25/doc-ifyqyuhy6343274.shtml\n",
      "1月25日 10:10 11省市违规围填海项目或被拆 有地方代企业缴罚款 http://news.sina.com.cn/c/sd/2018-01-25/doc-ifyqyesy1225724.shtml\n",
      "1月25日 07:42 一共几个猴？武汉大学长江学者“造假”纷争背后 http://news.sina.com.cn/c/sd/2018-01-25/doc-ifyqyuhy6201774.shtml\n",
      "1月23日 07:45 儿童邪典视频如何通过网站审核:或因考核压力惹祸 http://news.sina.com.cn/c/sd/2018-01-23/doc-ifyqwiqi5646294.shtml\n",
      "1月23日 03:03 在线教育打开你知识空间:1.44亿用户 1941亿市场 http://news.sina.com.cn/c/2018-01-23/doc-ifyquixe6490193.shtml\n",
      "1月22日 08:20 最穷上市公司未交60元年费 官网域名被人抢注转卖 http://news.sina.com.cn/c/sd/2018-01-22/doc-ifyquptv8499452.shtml\n",
      "1月22日 07:48 二三线城市购房政策微调 有城市正为去库存而焦虑 http://news.sina.com.cn/c/sd/2018-01-22/doc-ifyqtycx1498917.shtml\n",
      "1月19日 08:45 大多数老人去世前需要特殊照护 临终关怀仍缺位 http://news.sina.com.cn/c/sd/2018-01-19/doc-ifyquptv7850678.shtml\n",
      "1月19日 07:56 飞机上打电话有多远？科技日报:隔着一个行业利益 http://news.sina.com.cn/c/sd/2018-01-19/doc-ifyquixe4294264.shtml\n",
      "1月18日 09:28 半月谈：政务公开渠道多干货少 各地无统一标准 http://news.sina.com.cn/c/sd/2018-01-18/doc-ifyqtwzu1488526.shtml\n",
      "1月16日 06:36 半月谈：面对文山会海基层瞎对付 数据用模板编 http://news.sina.com.cn/o/2018-01-16/doc-ifyqqciz7673857.shtml\n",
      "1月10日 13:38 云南镇雄3死4伤凶案：毫无征兆 全家未曾与人争执 http://news.sina.com.cn/o/2018-01-10/doc-ifyqqciz4459048.shtml\n",
      "1月10日 13:30 毛振华事件令东北振兴话题再活跃：谁在抄底东北 http://news.sina.com.cn/c/sd/2018-01-10/doc-ifyqnick2145259.shtml\n",
      "1月10日 05:07 河南商水三中暗战:公务员用假文件将民办变公办 http://news.sina.com.cn/o/2018-01-10/doc-ifyqiwuw8922082.shtml\n"
     ]
    }
   ],
   "source": [
    "for news in soup.select('.news-item'):\n",
    "    if len(news.select('h2'))> 0:\n",
    "        h2=news.select('h2')[0].text\n",
    "        a = news.select('a')[0]['href']\n",
    "        time=news.select('.time')[0].text\n",
    "        print(time,h2,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html>\n",
      "<!-- [ published at 2018-02-19 17:59:53 ] -->\n",
      "<!-- LLTJ_MT:name =\"中国新闻网\" -->\n",
      "\n",
      "<html>\n",
      "<head>\n",
      "<meta charset=\"utf-8\"/>\n",
      "<meta http-equiv=\"Content-type\" content=\"text/html; charset=utf-8\" />\n",
      "<meta name=\"sudameta\" content=\"urlpath:c/; allCIDs:56044,257,51895,200856,51922,56261,258,38790\">\n",
      "<title>四川雅江森林火灾系人为失火 嫌疑人已被控制(图)|森林火灾|扑救|失火_新浪新闻</title>\n",
      "<meta name=\"keywords\" content=\"森林火灾,扑救,失火\" />\n",
      "<meta name=\"tags\" content=\"森林火灾,扑救,失火\" />\n",
      "<meta name=\"description\" content=\"原标题：四川雅江“2.16”森林火灾系人为失火扑救还在进行中新网成都2月19日电（刘忠俊杨俊辉郭振）记者18日从四川雅江恶古乡马益西村“2.16”森林火灾扑救指挥部获悉，恶古乡马益西村的森林火灾已持续燃烧了50余个小时，过火面积约100公顷\" />\n",
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      "<meta name=\"weibo: article:create_at\" content=\"2018-02-19 17:29:00\" />\n",
      "<meta name=\"weibo: article:update_at\" content=\"2018-02-19 17:59:53\" />\n",
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      "<meta property=\"article:published_time\" content=\"2018-02-19T17:34:18+08:00\" />\n",
      "<meta property=\"article:author\" content=\"中国新闻网\" />\n",
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      "\t<h1 class=\"main-title\">四川雅江森林火灾系人为失火 嫌疑人已被控制(图)</h1>\n",
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      "                    <span class=\"date\">2018年02月19日 17:29</span>\n",
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      "\t\t\t<div class=\"article\" id=\"article\">\n",
      "\t\t\t\t<p>　　原标题：<a href=\"http://news.sina.com.cn/c/nd/2018-02-19/doc-ifyrrmye2680732.shtml\" target=\"_blank\">四川雅江“2.16”森林火灾</a>系人为失火 扑救还在进行</p>\n",
      "<div class=\"img_wrapper\"><img src=\"http://n.sinaimg.cn/translate/w540h335/20180219/J8jc-fyrswmu2637888.jpg\" alt=\"雅江熊熊燃烧的森林火灾现场。　李传永　摄\" data-mcesrc=\"http://n.sinaimg.cn/translate/w540h335/20180219/J8jc-fyrswmu2637888.jpg\" data-mceselected=\"1\" data-link=\"\"><span class=\"img_descr\">雅江熊熊燃烧的森林火灾现场。　李传永　摄</span></div>\n",
      "<p>　　中新网成都2月19日电 （刘忠俊 杨俊辉 郭振）记者18日从四川雅江恶古乡马益西村“2.16”森林火灾扑救指挥部获悉，恶古乡马益西村的森林火灾已持续燃烧了50余个小时，过火面积约100公顷，火场周边村民已转移至安全地带。</p>\n",
      "<div class=\"img_wrapper\"><img src=\"http://n.sinaimg.cn/translate/w540h330/20180219/LvUJ-fyrswmu2637922.jpg\" alt=\"救援人员正在全力扑救。　李传永 摄\" data-mcesrc=\"http://n.sinaimg.cn/translate/w540h330/20180219/LvUJ-fyrswmu2637922.jpg\" data-mceselected=\"1\" data-link=\"\"><span class=\"img_descr\">救援人员正在全力扑救。　李传永 摄</span></div>\n",
      "<p>　　通报称，火场已调集森警、专业扑火队和林业系统干部职工、民众约1513人投入火灾扑救，其中森警294人，1架卡-32直升机采取接力水泵、直升机吊桶和开设隔离带相结合的方式参与扑救，新增援的86名森警预计16时到达火场，国家防火办紧急增调的3架卡-32直升机正在调机途中。</p>\n",
      "<p>　　“森警官兵主要担负火场西南、西北及东线扑救任务。”“2.16”森林火灾扑救指挥部相关负责人表示，此次森林大火由于受地形影响火场风向多变，瞬间风力最大可达9级，地表火、树冠火相互交织呈立体燃烧态势蔓延。</p>\n",
      "<p>　　据了解，参与灭火的森警官兵将分别在西南线、东线附近水源位置架设水泵，灵活采用“一点突破，两翼推进”战法，采取分段布控、风水灭火相结合的方式阻截林火蔓延。由于火场西北线风力迅速增大，森警官兵难以直接扑打，灭火指挥部决定实施直升机吊桶作业方式，将林火控制在西北一线。至18日晚22时，经过救援人员的全力奋战扑火，已扑灭明火1200余米，清理火线3000余米，处置明火点70余处、地下火15处、烟点280余处、站杆倒木90余处，飞火1处。</p>\n",
      "<div class=\"img_wrapper\"><img src=\"http://n.sinaimg.cn/translate/w540h369/20180219/du4W-fyrswmu2637965.jpg\" alt=\"直升机正在雅砻江取水灭火。南方航空护林总站\" data-mcesrc=\"http://n.sinaimg.cn/translate/w540h369/20180219/du4W-fyrswmu2637965.jpg\" data-mceselected=\"1\" data-link=\"\"><span class=\"img_descr\">直升机正在雅砻江取水灭火。南方航空护林总站</span></div>\n",
      "<p>　　目前，由于雅江森林火灾现场山高林密、地形复杂、气候无常，扑火战斗仍在紧张进行。经初步调查查明，此次火灾为人为失火，嫌疑人已被警方控制，正在进一步调查。</p>\n",
      "\t\t\t\t\n",
      "<p class=\"show_author\">责任编辑：张玉 </p>\n",
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      "        \t<a href=\"//blog.sina.com.cn/s/blog_5054769e0102xrkq.html\" target=\"_blank\">马未都：“怼”字任何字典里都没查到</a>\n",
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      "                <span class=\"title\">通过率低于20%的试炼，敢来吗？</span>\n",
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      "                <span class=\"title\">测测你的2017新闻指数有多高</span>\n",
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   "source": [
    "import requests\n",
    "res=requests.get('http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189473.shtml')\n",
    "res.encoding='utf-8'\n",
    "print(res.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "四川雅江森林火灾系人为失火 嫌疑人已被控制(图) 2018年02月19日 17:29\n",
      "中国新闻网 张玉  [<div class=\" sina-comment-wrap\" id=\"bottom_sina_comment\">\n",
      "</div>]\n"
     ]
    }
   ],
   "source": [
    "soup=BeautifulSoup(res.text,'html.parser')\n",
    "a=soup.select('.main-title')[0].text\n",
    "b=soup.select('.date-source ')[0].text.strip()\n",
    "author=soup.select('.show_author')[0].text.strip('责任编辑：')\n",
    "\n",
    "c=soup.select('#bottom_sina_comment')\n",
    "print(a,b,author,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-02-19 17:29:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "c=str('2018年02月19日 17:29')\n",
    "dt=datetime.strptime(c,'%Y年%m月%d日 %H:%M')\n",
    "print(dt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['原标题：四川雅江“2.16”森林火灾系人为失火 扑救还在进行', '中新网成都2月19日电 （刘忠俊 杨俊辉 郭振）记者18日从四川雅江恶古乡马益西村“2.16”森林火灾扑救指挥部获悉，恶古乡马益西村的森林火灾已持续燃烧了50余个小时，过火面积约100公顷，火场周边村民已转移至安全地带。', '通报称，火场已调集森警、专业扑火队和林业系统干部职工、民众约1513人投入火灾扑救，其中森警294人，1架卡-32直升机采取接力水泵、直升机吊桶和开设隔离带相结合的方式参与扑救，新增援的86名森警预计16时到达火场，国家防火办紧急增调的3架卡-32直升机正在调机途中。', '“森警官兵主要担负火场西南、西北及东线扑救任务。”“2.16”森林火灾扑救指挥部相关负责人表示，此次森林大火由于受地形影响火场风向多变，瞬间风力最大可达9级，地表火、树冠火相互交织呈立体燃烧态势蔓延。', '据了解，参与灭火的森警官兵将分别在西南线、东线附近水源位置架设水泵，灵活采用“一点突破，两翼推进”战法，采取分段布控、风水灭火相结合的方式阻截林火蔓延。由于火场西北线风力迅速增大，森警官兵难以直接扑打，灭火指挥部决定实施直升机吊桶作业方式，将林火控制在西北一线。至18日晚22时，经过救援人员的全力奋战扑火，已扑灭明火1200余米，清理火线3000余米，处置明火点70余处、地下火15处、烟点280余处、站杆倒木90余处，飞火1处。', '目前，由于雅江森林火灾现场山高林密、地形复杂、气候无常，扑火战斗仍在紧张进行。经初步调查查明，此次火灾为人为失火，嫌疑人已被警方控制，正在进一步调查。']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'原标题：四川雅江“2.16”森林火灾系人为失火 扑救还在进行@ 中新网成都2月19日电 （刘忠俊 杨俊辉 郭振）记者18日从四川雅江恶古乡马益西村“2.16”森林火灾扑救指挥部获悉，恶古乡马益西村的森林火灾已持续燃烧了50余个小时，过火面积约100公顷，火场周边村民已转移至安全地带。@ 通报称，火场已调集森警、专业扑火队和林业系统干部职工、民众约1513人投入火灾扑救，其中森警294人，1架卡-32直升机采取接力水泵、直升机吊桶和开设隔离带相结合的方式参与扑救，新增援的86名森警预计16时到达火场，国家防火办紧急增调的3架卡-32直升机正在调机途中。@ “森警官兵主要担负火场西南、西北及东线扑救任务。”“2.16”森林火灾扑救指挥部相关负责人表示，此次森林大火由于受地形影响火场风向多变，瞬间风力最大可达9级，地表火、树冠火相互交织呈立体燃烧态势蔓延。@ 据了解，参与灭火的森警官兵将分别在西南线、东线附近水源位置架设水泵，灵活采用“一点突破，两翼推进”战法，采取分段布控、风水灭火相结合的方式阻截林火蔓延。由于火场西北线风力迅速增大，森警官兵难以直接扑打，灭火指挥部决定实施直升机吊桶作业方式，将林火控制在西北一线。至18日晚22时，经过救援人员的全力奋战扑火，已扑灭明火1200余米，清理火线3000余米，处置明火点70余处、地下火15处、烟点280余处、站杆倒木90余处，飞火1处。@ 目前，由于雅江森林火灾现场山高林密、地形复杂、气候无常，扑火战斗仍在紧张进行。经初步调查查明，此次火灾为人为失火，嫌疑人已被警方控制，正在进一步调查。'"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "article=[]\n",
    "for p in soup.select('#article p')[:-1]:\n",
    "    article.append(p.text.strip())\n",
    "print(article)\n",
    "'@ '.join(article)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'原标题：四川雅江“2.16”森林火灾系人为失火 扑救还在进行 中新网成都2月19日电 （刘忠俊 杨俊辉 郭振）记者18日从四川雅江恶古乡马益西村“2.16”森林火灾扑救指挥部获悉，恶古乡马益西村的森林火灾已持续燃烧了50余个小时，过火面积约100公顷，火场周边村民已转移至安全地带。 通报称，火场已调集森警、专业扑火队和林业系统干部职工、民众约1513人投入火灾扑救，其中森警294人，1架卡-32直升机采取接力水泵、直升机吊桶和开设隔离带相结合的方式参与扑救，新增援的86名森警预计16时到达火场，国家防火办紧急增调的3架卡-32直升机正在调机途中。 “森警官兵主要担负火场西南、西北及东线扑救任务。”“2.16”森林火灾扑救指挥部相关负责人表示，此次森林大火由于受地形影响火场风向多变，瞬间风力最大可达9级，地表火、树冠火相互交织呈立体燃烧态势蔓延。 据了解，参与灭火的森警官兵将分别在西南线、东线附近水源位置架设水泵，灵活采用“一点突破，两翼推进”战法，采取分段布控、风水灭火相结合的方式阻截林火蔓延。由于火场西北线风力迅速增大，森警官兵难以直接扑打，灭火指挥部决定实施直升机吊桶作业方式，将林火控制在西北一线。至18日晚22时，经过救援人员的全力奋战扑火，已扑灭明火1200余米，清理火线3000余米，处置明火点70余处、地下火15处、烟点280余处、站杆倒木90余处，飞火1处。 目前，由于雅江森林火灾现场山高林密、地形复杂、气候无常，扑火战斗仍在紧张进行。经初步调查查明，此次火灾为人为失火，嫌疑人已被警方控制，正在进一步调查。'"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join([p.text.strip() for p in soup.select('#article p')[:-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "author=soup.select('show_author')\n",
    "print(author)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "re=requests.get('http://comment5.news.sina.com.cn/page/info?version=1&format=json&\\\n",
    "channel=gn&newsid=comos-fyrqwkc8189473&group=undefined&compress=0&ie=utf-8&oe=utf-8&\\\n",
    "page=1&page_size=3&t_size=3&h_size=3&thread=1')\n",
    "re.encoding='utf-8'\n",
    "import json\n",
    "jd=json.loads(re.text.strip('var data='))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd['result']['count']['total']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fyrqwkc8189473\n"
     ]
    }
   ],
   "source": [
    "newurl='http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189473.shtml'\n",
    "newid=newurl.split('/')[-1].lstrip('doc-i').rstrip('.shtml')\n",
    "print(newid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fyrqwkc8189473'"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "m=re.search('doc-i(.*).shtml',newurl)\n",
    "newsid=m.group(1)\n",
    "newsid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://comment5.news.sina.com.cn/page/info?version=1&format=json&channel=gn&newsid=comos-fyrqwkc8189473&group=undefined&compress=0&ie=utf-8&oe=utf-8&page=1&page_size=3&t_size=3&h_size=3&thread=1'"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commentURL ='http://comment5.news.sina.com.cn/page/info?version=1&format=json&channel=gn&newsid=comos-{}&group=undefined&compress=0&ie=utf-8&oe=utf-8&page=1&page_size=3&t_size=3&h_size=3&thread=1'\n",
    "commentURL.format(newsid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import json\n",
    "def getCommentCount(newsurl):\n",
    "    m=re.search('doc-i(.*).shtml',newsurl)\n",
    "    newsid=m.group(1)\n",
    "    comments=requests.get(commentURL.format(newsid))\n",
    "    jd=json.loads(comments.text.strip('var data='))\n",
    "    return jd['result']['count']['total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news='http://news.sina.com.cn/o/2018-02-19/doc-ifyrqwkc8189473.shtml'\n",
    "getCommentCount(news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "def getNewsDetail(newsurl):\n",
    "    result={}\n",
    "    res=requests.get(newsurl)\n",
    "    res.encoding='utf-8'\n",
    "    soup=BeautifulSoup(res.text,'html.parser')\n",
    "    result['title']=a=soup.select('.main-title')[0].text\n",
    "    result['newssource']=soup.select('#article p ')[0].text.strip().lstrip('原标题：')\n",
    "    result['dt']=soup.select('.date-source ')[0].text.strip()\n",
    "    result['article']=' '.join([p.text.strip() for p in soup.select('#article p')[:-1]])\n",
    "    result['author']=soup.select('.show_author')[0].text.strip('责任编辑：')\n",
    "    result['comments']=getCommentCount(newsurl)\n",
    "    return result\n",
    "import re\n",
    "import json\n",
    "def getCommentCount(newsurl):\n",
    "    m=re.search('doc-i(.*).shtml',newsurl)\n",
    "    newsid=m.group(1)\n",
    "    comments=requests.get(commentURL.format(newsid))\n",
    "    jd=json.loads(comments.text.strip('var data='))\n",
    "    return jd['result']['count']['total']\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'article': '原标题：两岸春节加班机“卡关” 台91岁老人跌下床妻子无机票返家照顾 [环球网综合报道]据台湾“中时电子报”2月17日报道，两岸春节包机因故卡关，造成许多台商或眷属都无法顺利返台过年。新北市新店区中生路有一位高龄91岁的张老先生，过年前在家中跌倒无法出门，引起邻居注意报警，经前往查看才知他因跌落床无法行动，且已多日未喝水及进食，而其妻则因机票问题而无法及时赶回照顾，于是，警察先将他送医再致赠其新北春节平安袋，预祝老人能早日出院返家。 新北市新店警察分局表示，青潭派出所两名警员日前执行巡逻勤务时，接获勤务中心通报在新店区中生路有民众报案称，已经数天未看见住在隔壁的张老先生（91岁）出门，于是警方循资料联系到老先生的妻子，但其妻表示，目前人还在大陆正等待侯补机位，暂时无法立即回台，因老人在台湾都没有其他的亲戚了，警方担心老先生发生什么意外，就马上主动通报消防队及里长前往现场，并自掏腰包请来锁匠开锁。 当警员、里长等人进入后，发现老人跌落床下无法起身及行动，而且已多日未喝水及进食，所幸老伯伯还有意识，众人即将他紧急送往耕莘医院救治，还好送医后无大碍，新店警事后也再前往医院关怀慰问，并送上里面装有白米、面条、罐头等民生物资的“春节平安袋”前来照顾的友人，同时预祝老先生能够早日出院返家。',\n",
       " 'author': '桂强 ',\n",
       " 'comments': 3212,\n",
       " 'dt': '2018年02月17日 15:41\\n环球网',\n",
       " 'newssource': '两岸春节加班机“卡关” 台91岁老人跌下床妻子无机票返家照顾',\n",
       " 'title': '台91岁老人跌下床 妻因两岸航班取消滞留大陆难回'}"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getNewsDetail('http://news.sina.com.cn/c/2018-02-17/doc-ifyrrhct8961136.shtml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.finance import candlestick_ochl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f11bf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xUeceNKFCAADopQMH3Kp6QpJ/nuTJST4uyedU1WNHNvvMJI8dft2S5AcPur/jpNNVADkS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\ncUuSW5Jkfn7+hltvvXWsmmb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HV9XrkjwnyT9J8oNV9ajW2ntaa3+T5JcyWNzoHyaDkYca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\nIlqB/d5zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x273ec4ccd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xUeceNKFCAADopQMH3Kp6QpJ/nuTJST4uyedU1WNHNvvMJI8dft2S5AcPur/jpNNVADkS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\nIlqB/d5zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f0f81860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    " \n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    " \n",
    "    \n",
    "code = '600273'\n",
    "start_data = '2016-05-18'\n",
    "end_data = '2018-05-18'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    " \n",
    " \n",
    " \n",
    "data_list = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    " \n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "fig.subplots_adjust(bottom=0.2)\n",
    " \n",
    " \n",
    "mpf.candlestick_ohlc(ax, data_list, width=1.5, colorup='r', colordown='green')\n",
    "plt.grid()\n",
    "ax.xaxis_date()\n",
    "ax.autoscale_view()\n",
    "plt.setp(plt.gca().get_xticklabels(),rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             open   high  close    low     volume\n",
      "date                                             \n",
      "2020-01-10  24.58  24.82  24.66  24.47   56476.32\n",
      "2020-01-13  24.73  24.78  24.58  24.41   45501.92\n",
      "2020-01-14  24.59  25.25  25.14  24.54  110317.10\n",
      "2020-01-15  25.20  25.22  24.78  24.75   58344.71\n",
      "2020-01-16  25.31  25.44  24.87  24.78   81102.00\n",
      "2020-01-17  24.89  24.89  24.67  24.53   45286.88\n",
      "2020-01-20  24.50  24.79  24.67  24.50   36187.11\n",
      "2020-01-21  24.59  24.64  23.99  23.95   61215.26\n",
      "2020-01-22  24.00  24.26  24.13  23.71   34321.94\n",
      "2020-01-23  24.02  24.25  23.59  23.53   56052.06\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f0fad7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "import matplotlib.pyplot as plt\n",
    "stock=ts.get_hist_data('600848') #一次性获取全部日k线数据\n",
    "#ts.get_hist_data('600848',start='2015-01-05',end='2015-01-09')\n",
    "stock=pd.DataFrame(stock)[::-1]\n",
    "#stock=stock.sort_values(by = 'date')\n",
    "print(stock.iloc[-10:,0:5])#最后10行\n",
    "#print(stock.iloc[:,:])\n",
    "#print(stock.ma5)\n",
    "stocklist=[]\n",
    "for i in range(len(stock.iloc[-10:,0:5])):\n",
    "    stocklist.append(stock.iloc[i,:])\n",
    "#print(stocklist)l\n",
    "fig, ax = plt.subplots()\n",
    "candlestick_ohlc(ax, stocklist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjHH3UaRGfb7dzC5tMr8Qtl8b4BrglxlmJ739clUI2/HrQD3wZJYmx/osROUn\nwJnASGALqcNJTSW9/W6i+b2S2LbdMb5Psi6WYVqz26+lhUnBD+ViZiWkfvFPuvtvms539zp33xs8\nnwuUmFmPuOpz983BYw3wNKnDCely2cZRmwgsdvetTWckvf0CWxsP/QWPNRnaJLodgxOuk4GbPTiI\n3lQOn4VIuPtWd29w96PAT7OsN7HtF3xvXA88la1NXNsuy/dJJJ+/lhYmBT2US3Cc9VFghbs/mKVN\nr8ZzOGY2mtTvcHtM9XUws06Nz0mdqF3WpNkc4LOWchGwu3GXOkZZ/1eY5PZLk/4Zmwr8LkObF4Hx\nZtYtOIwzPpgWOTObANwFXOPu+7O0yeWzEFV96efg/irLenP5W4/KFcBKd6/ONDOubdfM90k0n78o\nexMU4g+p3karSfX0+How7T5SfzgA7UgdHlkDvAUMjrG2S0jtSr4NLAl+JgG3AbcFbe4AKkn1TnkD\nuDjG+gYH610a1NC4/dLrM1I3P3sXeAcoj/n3255UOHRJm5bY9iMValuAI6T+t3crqXNwLwNVwWP3\noG058Ejasl8IPodrgM/HWN8aUsfLGz+Djb0b+wBzm/ssxFTfE8Fn621SX4y9m9YXvP7Y33rUtQXT\nH2v8vKW1TWLbZfs+ieTzp+FUREQktJZ2mEtERCKgMBERkdAUJiIiEprCREREQlOYiIhIaAoTEREJ\nTWEiIiKh/X/9r6RUAy/iTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f0f495c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([5,1,1,2,1,1],[20,10,10,20,10,10])\n",
    "plt.ylabel(\"good\")\n",
    "#plt.savefig(\"test\",)\n",
    "plt.axis([-1,20,0,20])\n",
    "#plt.subplot(3,2,3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncrG91ttkxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f01fdcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(10,10*1.5,10,10*2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            open  high  close  low  volume  price_change  p_change  ma5  ma10  \\\n",
      "date                                                                            \n",
      "2020-01-23   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-22   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-21   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-20   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-17   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-16   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-15   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-14   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-13   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-10   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-09   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-08   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-07   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "2020-01-06   NaN   NaN    NaN  NaN     NaN           NaN       NaN  NaN   NaN   \n",
      "\n",
      "            ma20  v_ma5  v_ma10  v_ma20  \n",
      "date                                     \n",
      "2020-01-23   NaN    NaN     NaN     NaN  \n",
      "2020-01-22   NaN    NaN     NaN     NaN  \n",
      "2020-01-21   NaN    NaN     NaN     NaN  \n",
      "2020-01-20   NaN    NaN     NaN     NaN  \n",
      "2020-01-17   NaN    NaN     NaN     NaN  \n",
      "2020-01-16   NaN    NaN     NaN     NaN  \n",
      "2020-01-15   NaN    NaN     NaN     NaN  \n",
      "2020-01-14   NaN    NaN     NaN     NaN  \n",
      "2020-01-13   NaN    NaN     NaN     NaN  \n",
      "2020-01-10   NaN    NaN     NaN     NaN  \n",
      "2020-01-09   NaN    NaN     NaN     NaN  \n",
      "2020-01-08   NaN    NaN     NaN     NaN  \n",
      "2020-01-07   NaN    NaN     NaN     NaN  \n",
      "2020-01-06   NaN    NaN     NaN     NaN  \n",
      "Index(['2020-01-23', '2020-01-22', '2020-01-21', '2020-01-20', '2020-01-17',\n",
      "       '2020-01-16', '2020-01-15', '2020-01-14', '2020-01-13', '2020-01-10',\n",
      "       '2020-01-09', '2020-01-08', '2020-01-07', '2020-01-06'],\n",
      "      dtype='object', name='date')\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'str' and 'float'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-66-d4a2dc75581c>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      8\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      9\u001B[0m \u001B[0mohlc\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mzip\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"open\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"high\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"low\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mbars\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"close\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 10\u001B[1;33m \u001B[0mcandlestick_ohlc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0max\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mohlc\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\matplotlib\\finance.py\u001B[0m in \u001B[0;36mcandlestick_ohlc\u001B[1;34m(ax, quotes, width, colorup, colordown, alpha)\u001B[0m\n\u001B[0;32m    737\u001B[0m     return _candlestick(ax, quotes, width=width, colorup=colorup,\n\u001B[0;32m    738\u001B[0m                         \u001B[0mcolordown\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcolordown\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 739\u001B[1;33m                         alpha=alpha, ochl=False)\n\u001B[0m\u001B[0;32m    740\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    741\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\matplotlib\\finance.py\u001B[0m in \u001B[0;36m_candlestick\u001B[1;34m(ax, quotes, width, colorup, colordown, alpha, ochl)\u001B[0m\n\u001B[0;32m    804\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    805\u001B[0m         rect = Rectangle(\n\u001B[1;32m--> 806\u001B[1;33m             \u001B[0mxy\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mt\u001B[0m \u001B[1;33m-\u001B[0m \u001B[0mOFFSET\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlower\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    807\u001B[0m             \u001B[0mwidth\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mwidth\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    808\u001B[0m             \u001B[0mheight\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mheight\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: unsupported operand type(s) for -: 'str' and 'float'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import date2num\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "import tushare as ts\n",
    "bars=ts.get_hist_data('002337',start='2020-01-05',end='2020-01-23')\n",
    "print(bars.shift(23))\n",
    "ax=plt.subplot2grid((4,4),(3,0),rowspan=3,colspan=4)\n",
    "print(bars.index)\n",
    "ohlc=zip(bars.index,bars[\"open\"],bars[\"high\"],bars[\"low\"],bars[\"close\"])\n",
    "candlestick_ohlc(ax,ohlc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ed0e7ed3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "2019-11-19\n",
      "2019-11-22\n",
      "2019-12-05\n",
      "2019-12-13\n",
      "2019-12-24\n",
      "2019-12-30\n",
      "2020-01-09\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    " \n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    " \n",
    "#显示   \n",
    "code = '300750' \n",
    "start_data = '2019-10-05'\n",
    "end_data = '2020-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "#print(hist_data)\n",
    "#print(hist_data.close-hist_data.open)\n",
    "  \n",
    "data_list = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    "#print(data_list) \n",
    "fig, ax = plt.subplots()\n",
    "fig.subplots_adjust(bottom=0.2)\n",
    " \n",
    "mpf.candlestick_ohlc(ax, data_list, width=0.7, colorup='r', colordown='green', alpha =.4)\n",
    "plt.grid()\n",
    "ax.xaxis_date()\n",
    "ax.autoscale_view()\n",
    "plt.setp(plt.gca().get_xticklabels(),rotation=30)\n",
    "plt.show()\n",
    "\n",
    "hist_data=hist_data[::-1]\n",
    "#print(hist_data.close)\n",
    "#print(hist_data.close.shift(1))\n",
    "clop=(hist_data.close/hist_data.close.shift(1)-1)*100\n",
    "#print(clop)\n",
    "Shape=[0,0,0]\n",
    "lag1clop=clop.shift(1)\n",
    "lag2clop=clop.shift(2)\n",
    "#print(lag2clop)\n",
    "for i in range(3,len(clop)):\n",
    "    #print(i)\n",
    "    if all([lag2clop[i]<0,abs(lag1clop[i])<3,clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5)]):\n",
    "        Shape.append(1)\n",
    "    else:\n",
    "        Shape.append(0)\n",
    "print(Shape)\n",
    "Shape_list=[]\n",
    "for i in range(len(clop)):\n",
    "    #print(i,hist_data.index[i],clop[i])\n",
    "    #if i==239:\n",
    "        #print(\"OK\",lag2clop[i]<0,abs(lag1clop[i]),clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5))\n",
    "    if all([Shape[i]==1]):\n",
    "        print(hist_data.index[i])\n",
    "        Shape_list.append(hist_data.index[i])\n",
    "if len(Shape_list)>0:\n",
    "    print(Shape_list[len(Shape_list)-1]<'2020-01-05')\n",
    "#Shape.index(1)\n",
    "#candleplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14 entries, 2020-01-23 to 2020-01-06\n",
      "Data columns (total 14 columns):\n",
      "open            14 non-null float64\n",
      "high            14 non-null float64\n",
      "close           14 non-null float64\n",
      "low             14 non-null float64\n",
      "volume          14 non-null float64\n",
      "price_change    14 non-null float64\n",
      "p_change        14 non-null float64\n",
      "ma5             14 non-null float64\n",
      "ma10            14 non-null float64\n",
      "ma20            14 non-null float64\n",
      "v_ma5           14 non-null float64\n",
      "v_ma10          14 non-null float64\n",
      "v_ma20          14 non-null float64\n",
      "时间              14 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(13)\n",
      "memory usage: 1.6+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14 entries, 2020-01-23 to 2020-01-06\n",
      "Data columns (total 14 columns):\n",
      "open            14 non-null float64\n",
      "high            14 non-null float64\n",
      "close           14 non-null float64\n",
      "low             14 non-null float64\n",
      "volume          14 non-null float64\n",
      "price_change    14 non-null float64\n",
      "p_change        14 non-null float64\n",
      "ma5             14 non-null float64\n",
      "ma10            14 non-null float64\n",
      "ma20            14 non-null float64\n",
      "v_ma5           14 non-null float64\n",
      "v_ma10          14 non-null float64\n",
      "v_ma20          14 non-null float64\n",
      "时间              14 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(13)\n",
      "memory usage: 1.6+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
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AG4BB4BMN/gwzs1eoNa/2wiMX1j+v9iyerrRR1xwukfQBYDtwWY3TQgH8UFIA/xARG1N5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x273f0e90dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    " \n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    "     \n",
    "code = '002911' \n",
    "start_data = '2020-01-05'\n",
    "end_data = '2020-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "hist_data['时间']=pd.to_datetime(hist_data.index,format=\"%Y/%m/%d\")\n",
    "print(hist_data.info())\n",
    "#hist_data['时间']=hist_data['时间'].values\n",
    "#apply(lambda x:dates.date2num(x)*1440)\n",
    "print(hist_data.info())\n",
    "data_list = []\n",
    "data_list_t1 = []\n",
    "data_list_t2 = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    v = row[4:5]\n",
    "    #print(v)\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    "    datas_t1 = (t)\n",
    "    data_list_t1.append(datas_t1)\n",
    "    datas_t2 = (v)\n",
    "    data_list_t2.append(datas_t2) \n",
    "    \n",
    "ax1 =plt.subplot2grid((4,4),(0,0),rowspan=3,colspan=4)\n",
    "mpf.candlestick_ohlc(ax1, data_list, width=0.7, colorup='r', colordown='green', alpha =.4)\n",
    "plt.grid()\n",
    "ax2 =plt.subplot2grid((4,4),(3,0),rowspan=1,colspan=4)\n",
    "#print(data_list_t)\n",
    "#ax2.bar(data_list_t1,data_list_t2,width=0.7)\n",
    "ax2.bar(hist_data['时间'].map(date2num),hist_data['volume'],width=0.7)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "import time\n",
    "env = gym.make('CartPole-v0')\n",
    "env.reset()\n",
    "time.sleep(2)\n",
    "\n",
    "def reset(self):\n",
    "    \"\"\" 重新初始化函数 \"\"\"\n",
    "    # 利用均匀随机分布初试化环境的状态 \n",
    "    self.state = self.np_random.uniform(low=-0.05, high=0.05, size=(4,))\n",
    "    \n",
    "    # 设置当前步数为None\n",
    "    self.steps_beyond_done = None\n",
    "    \n",
    "    # 返回环境的初始化状态\n",
    "    return np.array(self.state)\n",
    "def render(self, mode='human'):\n",
    "    \"\"\" render()函数在这里扮演图像引擎的角色。一个仿真环境必不可少的两部分是物理引擎和图像引擎。物理引擎模拟环境中物体的运动规律；图像引擎用来显示环境中的物体图像 \"\"\"\n",
    "    screen_width = 600\n",
    "    screen_height = 400\n",
    " \n",
    "    world_width = self.x_threshold*2\n",
    "    scale = screen_width/world_width\n",
    "    carty = 100 # TOP OF CART\n",
    "    polewidth = 10.0\n",
    "    polelen = scale * 1.0\n",
    "    cartwidth = 50.0\n",
    "    cartheight = 30.0\n",
    " \n",
    "    if self.viewer is None:\n",
    "        # 导入rendering模块，利用rendering模块中的画图函数进行图形的绘制\n",
    "        from gym.envs.classic_control import rendering\n",
    " \n",
    "        # 绘制600*400的窗口函数为screen_width*screen_height\n",
    "        self.viewer = rendering.Viewer(screen_width, screen_height)\n",
    "        \n",
    "        # 创建cart矩形,rendering.FilledPolygon为填充一个矩形\n",
    "        l,r,t,b = -cartwidth/2, cartwidth/2, cartheight/2, -cartheight/2\n",
    "        axleoffset =cartheight/4.0\n",
    "        cart = rendering.FilledPolygon([(l,b), (l,t), (r,t), (r,b)])\n",
    "        # Transform给cart添加平移属性和旋转属性\n",
    "        self.carttrans = rendering.Transform()\n",
    "        cart.add_attr(self.carttrans)\n",
    "        # 在图上加入几何cart\n",
    "        self.viewer.add_geom(cart)\n",
    " \n",
    "        # 创建摆杆pole\n",
    "        l,r,t,b = -polewidth/2,polewidth/2,polelen-polewidth/2,-polewidth/2\n",
    "        pole = rendering.FilledPolygon([(l,b), (l,t), (r,t), (r,b)])        \n",
    "        # 给pole设置颜色\n",
    "        pole.set_color(.8,.6,.4)\n",
    "        # 添加摆杆转换矩阵属性\n",
    "        self.poletrans = rendering.Transform(translation=(0, axleoffset))\n",
    "        pole.add_attr(self.poletrans)\n",
    "        pole.add_attr(self.carttrans)\n",
    "        # 加入几何体\n",
    "        self.viewer.add_geom(pole)\n",
    "        \n",
    "        # 创建摆杆和台车之间的连接\n",
    "        self.axle = rendering.make_circle(polewidth/2)\n",
    "        self.axle.add_attr(self.poletrans)\n",
    "        self.axle.add_attr(self.carttrans)\n",
    "        self.axle.set_color(.5,.5,.8)\n",
    "        self.viewer.add_geom(self.axle)\n",
    "        \n",
    "        #创建台车来回滑动的轨道，即一条直线\n",
    "        self.track = rendering.Line((0,carty), (screen_width,carty))\n",
    "        self.track.set_color(0,0,0)\n",
    "        self.viewer.add_geom(self.track)\n",
    " \n",
    "    if self.state is None: return None\n",
    " \n",
    "    # 设置平移属性\n",
    "    x = self.state\n",
    "    cartx = x[0]*scale+screen_width/2.0 # MIDDLE OF CART\n",
    "    self.carttrans.set_translation(cartx, carty)\n",
    "    self.poletrans.set_rotation(-x[2])\n",
    " \n",
    "    return self.viewer.render(return_rgb_array = mode=='rgb_array')\n",
    "def step(self, action):\n",
    "    \"\"\" 该函数在仿真器中扮演物理引擎的角色。其输入是动作a，输出是：下一步状态，立即回报，是否终止，调试项。该函数描述了智能体与环境交互的所有信息，是环境文件中最重要的函数。在该函数中，一般利用智能体的运动学模型和动力学模型计算下一步的状态和立即回报，并判断是否达到终止状态 \"\"\"\n",
    "    assert self.action_space.contains(action), \"%r (%s) invalid\"%(action, type(action))\n",
    "    state = self.state\n",
    "    # 系统的当前状态\n",
    "    x, x_dot, theta, theta_dot = state\n",
    "    # 输入动作，即作用到车上的力\n",
    "    force = self.force_mag if action==1 else -self.force_mag\n",
    "    # 余弦函数\n",
    "    costheta = math.cos(theta)\n",
    "    # 正弦函数\n",
    "    sintheta = math.sin(theta)\n",
    "    # 车摆的动力学方程式，即加速度与动作之间的关系\n",
    "    temp = (force + self.polemass_length * theta_dot * theta_dot * sintheta) / self.total_mass\n",
    "    # 摆的角加速度\n",
    "    thetaacc = (self.gravity * sintheta - costheta* temp) / (self.length * (4.0/3.0 - self.masspole * costheta * costheta / self.total_mass))\n",
    "    # 小车的平加速度\n",
    "    xacc  = temp - self.polemass_length * thetaacc * costheta / self.total_mass\n",
    "    # tau是更新步长0.02，下面是计算下一步的状态\n",
    "    x  = x + self.tau * x_dot\n",
    "    x_dot = x_dot + self.tau * xacc\n",
    "    theta = theta + self.tau * theta_dot\n",
    "    theta_dot = theta_dot + self.tau * thetaacc\n",
    "    self.state = (x,x_dot,theta,theta_dot)\n",
    "    # 设定小车和摆杆的阈值\n",
    "    done =  x < -self.x_threshold \\\n",
    "            or x > self.x_threshold \\\n",
    "            or theta < -self.theta_threshold_radians \\\n",
    "            or theta > self.theta_threshold_radians\n",
    "    done = bool(done)\n",
    " \n",
    "    # \n",
    "    if not done:\n",
    "        reward = 1.0\n",
    "    elif self.steps_beyond_done is None:\n",
    "        # Pole just fell!\n",
    "        self.steps_beyond_done = 0\n",
    "        reward = 1.0\n",
    "    else:\n",
    "        if self.steps_beyond_done == 0:\n",
    "            logger.warn(\"You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\")\n",
    "        self.steps_beyond_done += 1\n",
    "        reward = 0.0\n",
    " \n",
    "    return np.array(self.state), reward, done, {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('CartPole-v0')\n",
    "env.reset()\n",
    "help()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "abstract",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-2-7b9f8de0bcc8>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      9\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mdone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     10\u001B[0m         \u001B[1;32mbreak\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 11\u001B[1;33m     \u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrender\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m         \u001B[1;31m#绘制场景\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     12\u001B[0m     \u001B[0mcount\u001B[0m\u001B[1;33m+=\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     13\u001B[0m     \u001B[0mtime\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msleep\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0.2\u001B[0m\u001B[1;33m)\u001B[0m      \u001B[1;31m#每次等待0.2s\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\gym\\core.py\u001B[0m in \u001B[0;36mrender\u001B[1;34m(self, mode, **kwargs)\u001B[0m\n\u001B[0;32m    231\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    232\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mrender\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'human'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 233\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrender\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    234\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    235\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mclose\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\gym\\envs\\classic_control\\cartpole.py\u001B[0m in \u001B[0;36mrender\u001B[1;34m(self, mode)\u001B[0m\n\u001B[0;32m    149\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mviewer\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    150\u001B[0m             \u001B[1;32mfrom\u001B[0m \u001B[0mgym\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0menvs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mclassic_control\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mrendering\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 151\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mviewer\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mrendering\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mViewer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mscreen_width\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mscreen_height\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    152\u001B[0m             \u001B[0ml\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mr\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mt\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mb\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m-\u001B[0m\u001B[0mcartwidth\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcartwidth\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcartheight\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m-\u001B[0m\u001B[0mcartheight\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    153\u001B[0m             \u001B[0maxleoffset\u001B[0m \u001B[1;33m=\u001B[0m\u001B[0mcartheight\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m4.0\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\gym\\envs\\classic_control\\rendering.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, width, height, display)\u001B[0m\n\u001B[0;32m     58\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwidth\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mwidth\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     59\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mheight\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mheight\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 60\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwindow\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpyglet\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwindow\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mWindow\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mwidth\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mwidth\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mheight\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mheight\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdisplay\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mdisplay\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     61\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwindow\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mon_close\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwindow_closed_by_user\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     62\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0misopen\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mTrue\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\pyglet\\window\\__init__.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, width, height, caption, resizable, style, fullscreen, visible, vsync, display, screen, config, context, mode)\u001B[0m\n\u001B[0;32m    502\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    503\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mscreen\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 504\u001B[1;33m             \u001B[0mscreen\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdisplay\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_default_screen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    505\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    506\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mconfig\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\pyglet\\canvas\\base.py\u001B[0m in \u001B[0;36mget_default_screen\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m     71\u001B[0m         \u001B[1;33m:\u001B[0m\u001B[0mrtype\u001B[0m\u001B[1;33m:\u001B[0m \u001B[1;33m:\u001B[0m\u001B[1;32mclass\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0;31m`\u001B[0m\u001B[0mScreen\u001B[0m\u001B[0;31m`\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     72\u001B[0m         '''\n\u001B[1;32m---> 73\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_screens\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     74\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     75\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mget_windows\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\site-packages\\pyglet\\canvas\\base.py\u001B[0m in \u001B[0;36mget_screens\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m     63\u001B[0m         \u001B[1;33m:\u001B[0m\u001B[0mrtype\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mlist\u001B[0m \u001B[0mof\u001B[0m \u001B[1;33m:\u001B[0m\u001B[1;32mclass\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0;31m`\u001B[0m\u001B[0mScreen\u001B[0m\u001B[0;31m`\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     64\u001B[0m         '''\n\u001B[1;32m---> 65\u001B[1;33m         \u001B[1;32mraise\u001B[0m \u001B[0mNotImplementedError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'abstract'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     66\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     67\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mget_default_screen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mNotImplementedError\u001B[0m: abstract"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import time\n",
    "env = gym.make('CartPole-v0')   #创造环境\n",
    "observation = env.reset()       #初始化环境，observation为环境状态\n",
    "count = 0\n",
    "for t in range(100):\n",
    "    action = env.action_space.sample()  #随机采样动作\n",
    "    observation, reward, done, info = env.step(action)  #与环境交互，获得下一步的时刻\n",
    "    if done:             \n",
    "        break\n",
    "    env.render()         #绘制场景\n",
    "    count+=1\n",
    "    time.sleep(0.2)      #每次等待0.2s\n",
    "print(count)             #打印该次尝试的步数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Getting data:]############################################################603920 20200123\n",
      "True ['2019-10-22', '2019-10-28', '2019-12-25', '2020-01-13', '2020-01-21']\n",
      "300562 20200123\n",
      "True ['2019-10-28', '2019-11-14', '2019-11-19', '2020-01-07', '2020-01-14', '2020-01-20']\n",
      "300671 20200123\n",
      "True ['2020-01-02', '2020-01-07']\n",
      "601698 20200123\n",
      "False ['2019-11-06']\n",
      "603185 20200123\n",
      "True ['2019-12-13', '2020-01-03', '2020-01-16']\n",
      "002635 20200123\n",
      "False ['2019-11-14', '2019-12-06']\n",
      "000955 20200123\n",
      "300578 20200123\n",
      "False ['2019-10-28']\n",
      "300513 20200123\n",
      "False ['2019-10-22', '2019-10-28', '2019-11-13']\n",
      "300303 20200123\n",
      "603238 20200123\n",
      "True ['2020-01-20']\n",
      "603138 20200123\n",
      "False ['2019-10-22', '2019-10-28', '2020-01-02']\n",
      "300687 20200123\n",
      "False ['2019-12-05', '2020-01-02']\n",
      "300312 20200123\n",
      "True ['2019-10-15', '2019-10-25', '2019-10-31', '2019-11-19', '2019-12-31', '2020-01-22']\n",
      "300551 20200123\n",
      "False ['2019-11-05', '2019-12-16', '2019-12-20', '2019-12-30']\n",
      "002837 20200123\n",
      "False ['2019-10-22']\n",
      "300693 20200123\n",
      "True ['2019-12-24', '2019-12-31', '2020-01-20']\n",
      "002214 20200123\n",
      "False ['2019-10-28', '2019-11-04']\n",
      "603536 20200123\n",
      "False ['2019-11-14']\n",
      "600171 20200123\n",
      "300448 20200123\n",
      "False ['2019-10-28', '2019-11-29', '2019-12-16']\n",
      "002907 20200123\n",
      "True ['2019-11-29', '2020-01-20']\n",
      "002510 20200123\n",
      "False ['2019-10-17', '2019-12-27']\n",
      "002119 20200123\n",
      "True ['2019-10-28', '2019-12-25', '2020-01-10', '2020-01-21']\n",
      "002291 20200123\n",
      "True ['2019-11-05', '2019-11-19', '2019-12-06', '2019-12-13', '2020-01-09']\n",
      "002079 20200123\n",
      "True ['2019-10-28', '2019-11-13', '2019-12-06', '2019-12-25', '2020-01-02', '2020-01-07', '2020-01-16']\n",
      "002125 20200123\n",
      "True ['2019-11-25', '2020-01-09']\n",
      "300230 20200123\n",
      "False ['2020-01-02']\n",
      "300373 20200123\n",
      "True ['2019-10-22', '2019-10-28', '2019-12-25', '2020-01-20']\n",
      "300256 20200123\n",
      "True ['2019-10-21', '2019-11-13', '2019-11-29', '2019-12-13', '2020-01-02', '2020-01-21']\n",
      "603279 20200123\n",
      "002855 20200123\n",
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      "002151 20200123\n",
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      "300246 20200123\n",
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      "300781 20200123\n",
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      "002467 20200123\n",
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      "300071 20200123\n",
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      "300184 20200123\n",
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      "002860 20200123\n",
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      "002367 20200123\n",
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      "300775 20200123\n",
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      "002600 20200123\n",
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      "300287 20200123\n",
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      "300726 20200123\n",
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      "300460 20200123\n",
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      "300705 20200123\n",
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      "300444 20200123\n",
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      "600267 20200123\n",
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      "300684 20200123\n",
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      "000790 20200123\n",
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      "002846 20200123\n",
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      "002888 20200123\n",
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      "603050 20200123\n",
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      "000021 20200123\n",
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      "300683 20200123\n",
      "300541 20200123\n",
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      "600613 20200123\n",
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      "603505 20200123\n",
      "300497 20200123\n",
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      "300390 20200123\n",
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      "300254 20200123\n",
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      "300656 20200123\n",
      "False ['2020-01-03']\n",
      "002865 20200123\n",
      "300231 20200123\n",
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      "600587 20200123\n",
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      "300690 20200123\n",
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      "002917 20200123\n",
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      "002923 20200123\n",
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      "300130 20200123\n",
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      "603676 20200123\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "688007 20200123\n",
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      "603600 20200123\n",
      "True ['2019-12-05', '2019-12-31', '2020-01-13']\n",
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      "603587 20200123\n",
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      "300024 20200123\n",
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      "300459 20200123\n",
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      "002504 20200123\n",
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      "002320 20200123\n",
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      "000721 20200123\n",
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      "000590 20200123\n",
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      "002312 20200123\n",
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      "002877 20200123\n",
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      "600603 20200123\n",
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      "600078 20200123\n",
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      "002948 20200123\n",
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      "603111 20200123\n",
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      "603998 20200123\n",
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      "002758 20200123\n",
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      "300662 20200123\n",
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      "002884 20200123\n",
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      "002111 20200123\n",
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      "600580 20200123\n",
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      "000622 20200123\n",
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      "300437 20200123\n",
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      "300743 20200123\n",
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      "601606 20200123\n",
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      "002644 20200123\n",
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      "600243 20200123\n",
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      "300371 20200123\n",
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      "002893 20200123\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "603168 20200123\n",
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      "000895 20200123\n",
      "True ['2019-11-08', '2020-01-07']\n",
      "600201 20200123\n",
      "False ['2019-10-30']\n",
      "000975 20200123\n",
      "False ['2019-11-13', '2019-12-04']\n",
      "002769 20200123\n",
      "False ['2019-11-04', '2019-11-12']\n",
      "600547 20200123\n",
      "False ['2019-11-13', '2019-12-04']\n",
      "300317 20200123\n",
      "False ['2019-11-22', '2019-12-10', '2019-12-16', '2019-12-24', '2019-12-27']\n",
      "300121 20200123\n",
      "True ['2019-12-02', '2019-12-13', '2020-01-10']\n",
      "603336 20200123\n",
      "False ['2019-11-20', '2019-11-26']\n",
      "300146 20200123\n",
      "False ['2019-12-27']\n",
      "002734 20200123\n",
      "False ['2019-10-17']\n",
      "600984 20200123\n",
      "False ['2019-11-04', '2019-11-27']\n",
      "002376 20200123\n",
      "600532 20200123\n",
      "False ['2019-10-21', '2019-11-19', '2019-11-25']\n",
      "000948 20200123\n",
      "002043 20200123\n",
      "False ['2019-12-18']\n",
      "000739 20200123\n",
      "True ['2019-10-18', '2019-11-14', '2019-12-13', '2020-01-07', '2020-01-16']\n",
      "300466 20200123\n",
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      "300065 20200123\n",
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      "603319 20200123\n",
      "002570 20200123\n",
      "True ['2019-10-14', '2019-10-17', '2019-11-27', '2020-01-10', '2020-01-16']\n",
      "002218 20200123\n",
      "True ['2019-12-25', '2020-01-07']\n",
      "603315 20200123\n",
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      "002349 20200123\n",
      "True ['2019-12-03', '2020-01-20']\n",
      "002424 20200123\n",
      "True ['2019-11-06', '2020-01-20']\n",
      "002788 20200123\n",
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      "002400 20200123\n",
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      "300218 20200123\n",
      "600105 20200123\n",
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      "002576 20200123\n",
      "002158 20200123\n",
      "False ['2019-11-07', '2019-12-24']\n",
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      "600461 20200123\n",
      "600326 20200123\n",
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      "000581 20200123\n",
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      "300048 20200123\n",
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      "603398 20200123\n",
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      "603179 20200123\n",
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      "002374 20200123\n",
      "600759 20200123\n",
      "000949 20200123\n",
      "False ['2019-11-12', '2020-01-02']\n",
      "600557 20200123\n",
      "True ['2019-11-07', '2020-01-21']\n",
      "000666 20200123\n",
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      "000550 20200123\n",
      "True ['2019-10-14', '2019-11-07', '2019-12-11', '2020-01-14']\n",
      "002155 20200123\n",
      "False ['2019-11-13', '2019-12-04']\n",
      "601777 20200123\n",
      "True ['2019-11-19', '2019-12-03', '2019-12-06', '2019-12-11', '2020-01-20']\n",
      "601577 20200123\n",
      "600056 20200123\n",
      "600531 20200123\n",
      "False ['2019-11-13', '2019-12-04']\n",
      "600740 20200123\n",
      "True ['2020-01-07']\n",
      "300424 20200123\n",
      "False ['2019-12-16']\n",
      "002610 20200123\n",
      "True ['2020-01-07']\n",
      "601012 20200123\n",
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      "002786 20200123\n",
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      "600522 20200123\n",
      "603299 20200123\n",
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      "002325 20200123\n",
      "002255 20200123\n",
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      "300757 20200123\n",
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      "002168 20200123\n",
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      "000795 20200123\n",
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      "000592 20200123\n",
      "True ['2020-01-06']\n",
      "002271 20200123\n",
      "True ['2019-10-25', '2020-01-16']\n",
      "002171 20200123\n",
      "002412 20200123\n",
      "True ['2019-10-11', '2020-01-21']\n",
      "600478 20200123\n",
      "True ['2019-12-24', '2020-01-15']\n",
      "002628 20200123\n",
      "600435 20200123\n",
      "600161 20200123\n",
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      "300440 20200123\n",
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      "000503 20200123\n",
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      "000563 20200123\n",
      "600909 20200123\n",
      "600197 20200123\n",
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      "300064 20200123\n",
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      "002087 20200123\n",
      "False ['2019-10-22']\n",
      "300150 20200123\n",
      "600933 20200123\n",
      "False ['2019-10-21', '2019-11-19', '2019-12-16']\n",
      "002832 20200123\n",
      "False ['2019-12-05', '2019-12-16']\n",
      "600353 20200123\n",
      "False ['2019-12-02', '2019-12-25']\n",
      "002511 20200123\n",
      "False ['2019-10-17', '2019-11-18']\n",
      "000727 20200123\n",
      "False ['2019-12-16']\n",
      "600706 20200123\n",
      "601598 20200123\n",
      "002310 20200123\n",
      "False ['2019-12-05']\n",
      "300224 20200123\n",
      "False ['2019-12-24']\n",
      "600198 20200123\n",
      "False ['2019-10-28', '2019-11-04', '2019-11-27', '2019-12-05', '2019-12-25']\n",
      "300291 20200123\n",
      "False ['2019-11-19', '2019-12-16', '2019-12-19', '2019-12-24']\n",
      "002452 20200123\n",
      "True ['2019-12-16', '2019-12-19', '2020-01-14']\n",
      "600325 20200123\n",
      "002632 20200123\n",
      "True ['2019-11-07', '2019-11-25', '2019-12-03', '2019-12-13', '2020-01-07']\n",
      "000720 20200123\n",
      "False ['2019-10-23']\n",
      "002121 20200123\n",
      "False ['2019-11-08']\n",
      "002406 20200123\n",
      "True ['2019-12-24', '2019-12-30', '2020-01-09']\n",
      "001896 20200123\n",
      "300145 20200123\n",
      "True ['2019-12-16', '2019-12-25', '2020-01-14']\n",
      "000993 20200123\n",
      "600589 20200123\n",
      "True ['2019-10-17', '2020-01-07']\n",
      "002879 20200123\n",
      "False ['2019-10-22', '2019-11-13', '2019-11-21', '2019-12-19']\n",
      "000686 20200123\n",
      "600775 20200123\n",
      "False ['2019-10-28']\n",
      "002164 20200123\n",
      "False ['2019-12-12']\n",
      "002430 20200123\n",
      "True ['2019-12-02', '2019-12-05', '2019-12-24', '2019-12-31', '2020-01-17']\n",
      "603227 20200123\n",
      "True ['2020-01-03', '2020-01-10']\n",
      "601168 20200123\n",
      "False ['2019-12-24']\n",
      "000541 20200123\n",
      "True ['2020-01-17']\n",
      "002539 20200123\n",
      "300294 20200123\n",
      "True ['2019-10-15', '2019-11-04', '2019-12-24', '2020-01-21']\n",
      "002664 20200123\n",
      "False ['2019-10-14', '2019-12-20', '2019-12-31']\n",
      "600387 20200123\n",
      "False ['2019-10-11', '2019-11-19', '2019-12-24']\n",
      "002181 20200123\n",
      "True ['2019-10-23', '2019-12-06', '2019-12-19', '2019-12-25', '2020-01-17']\n",
      "002011 20200123\n",
      "True ['2019-10-23', '2019-12-23', '2020-01-20']\n",
      "300266 20200123\n",
      "False ['2019-11-19']\n",
      "002728 20200123\n",
      "True ['2020-01-21']\n",
      "000635 20200123\n",
      "False ['2019-10-30']\n",
      "600903 20200123\n",
      "False ['2019-11-25', '2019-12-04']\n",
      "002297 20200123\n",
      "300381 20200123\n",
      "True ['2019-11-27', '2020-01-21']\n",
      "600566 20200123\n",
      "True ['2019-12-06', '2020-01-20']\n",
      "600576 20200123\n",
      "True ['2020-01-07']\n",
      "000536 20200123\n",
      "False ['2019-12-04', '2019-12-16']\n",
      "300262 20200123\n",
      "True ['2019-10-25', '2019-12-16', '2020-01-14']\n",
      "300531 20200123\n",
      "False ['2019-10-28']\n",
      "600029 20200123\n",
      "300055 20200123\n",
      "False ['2019-11-07', '2019-11-12', '2019-11-19']\n",
      "603819 20200123\n",
      "False ['2019-10-22', '2019-11-19']\n",
      "002448 20200123\n",
      "False ['2019-10-17']\n",
      "002233 20200123\n",
      "False ['2019-11-19']\n",
      "600776 20200123\n",
      "False ['2019-11-19', '2019-12-17']\n",
      "002262 20200123\n",
      "600579 20200123\n",
      "False ['2019-11-19']\n",
      "600864 20200123\n",
      "603988 20200123\n",
      "False ['2019-11-28', '2019-12-24']\n",
      "002592 20200123\n",
      "False ['2019-11-19']\n",
      "600995 20200123\n",
      "002550 20200123\n",
      "603727 20200123\n",
      "True ['2019-12-25', '2019-12-31', '2020-01-13']\n",
      "000681 20200123\n",
      "True ['2019-10-28', '2019-11-08', '2019-11-18', '2019-12-31', '2020-01-13']\n",
      "603668 20200123\n",
      "False ['2019-11-12']\n",
      "603357 20200123\n",
      "False ['2019-11-19']\n",
      "002141 20200123\n",
      "002418 20200123\n",
      "False ['2019-11-01', '2019-11-19', '2019-11-29']\n",
      "002626 20200123\n",
      "True ['2019-12-16', '2019-12-25', '2020-01-07']\n",
      "002211 20200123\n",
      "False ['2019-10-22', '2019-12-20']\n",
      "002330 20200123\n",
      "True ['2019-10-21', '2019-11-08', '2019-12-06', '2019-12-17', '2020-01-07']\n",
      "600196 20200123\n",
      "False ['2019-11-07']\n",
      "600351 20200123\n",
      "600636 20200123\n",
      "True ['2020-01-17']\n",
      "300034 20200123\n",
      "False ['2019-11-12']\n",
      "000411 20200123\n",
      "002196 20200123\n",
      "True ['2020-01-14']\n",
      "002067 20200123\n",
      "False ['2019-12-24', '2019-12-30']\n",
      "300490 20200123\n",
      "True ['2019-12-12', '2020-01-14']\n",
      "600730 20200123\n",
      "True ['2019-12-03', '2020-01-21']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "002506 20200123\n",
      "False ['2019-12-10']\n",
      "600416 20200123\n",
      "600120 20200123\n",
      "True ['2020-01-15']\n",
      "000045 20200123\n",
      "False ['2019-11-01']\n",
      "601616 20200123\n",
      "600604 20200123\n",
      "False ['2019-12-10']\n",
      "000881 20200123\n",
      "True ['2020-01-14']\n",
      "603020 20200123\n",
      "False ['2019-11-01']\n",
      "002431 20200123\n",
      "False ['2019-10-22']\n",
      "000926 20200123\n",
      "600977 20200123\n",
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      "600113 20200123\n",
      "601128 20200123\n",
      "False ['2019-12-31']\n",
      "000524 20200123\n",
      "002215 20200123\n",
      "600525 20200123\n",
      "600491 20200123\n",
      "False ['2019-11-13']\n",
      "601619 20200123\n",
      "False ['2019-10-28']\n",
      "600268 20200123\n",
      "False ['2019-12-19']\n",
      "300318 20200123\n",
      "True ['2019-10-21', '2019-10-30', '2020-01-20']\n",
      "002650 20200123\n",
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      "300516 20200123\n",
      "False ['2019-11-15', '2020-01-02']\n",
      "300289 20200123\n",
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      "600172 20200123\n",
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      "300369 20200123\n",
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      "300157 20200123\n",
      "000777 20200123\n",
      "002154 20200123\n",
      "False ['2019-12-30']\n",
      "600375 20200123\n",
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      "300003 20200123\n",
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      "002708 20200123\n",
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      "002451 20200123\n",
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      "002585 20200123\n",
      "000068 20200123\n",
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      "000543 20200123\n",
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      "300105 20200123\n",
      "False ['2019-10-21', '2019-10-25']\n",
      "000403 20200123\n",
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      "002478 20200123\n",
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      "002743 20200123\n",
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      "002785 20200123\n",
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      "601011 20200123\n",
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      "000619 20200123\n",
      "002443 20200123\n",
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      "600882 20200123\n",
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      "601375 20200123\n",
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      "600318 20200123\n",
      "600853 20200123\n",
      "002653 20200123\n",
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      "000902 20200123\n",
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      "002004 20200123\n",
      "True ['2019-12-06', '2020-01-20']\n",
      "600819 20200123\n",
      "603968 20200123\n",
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      "002491 20200123\n",
      "300240 20200123\n",
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      "600367 20200123\n",
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      "600505 20200123\n",
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      "600073 20200123\n",
      "False ['2019-10-25']\n",
      "000905 20200123\n",
      "True ['2020-01-10']\n",
      "002842 20200123\n",
      "True ['2020-01-14']\n",
      "600380 20200123\n",
      "True ['2019-11-01', '2019-11-07', '2019-12-24', '2020-01-20']\n",
      "600707 20200123\n",
      "False ['2019-12-09', '2019-12-16']\n",
      "000848 20200123\n",
      "600868 20200123\n",
      "600079 20200123\n",
      "True ['2020-01-16']\n",
      "603027 20200123\n",
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      "002476 20200123\n",
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      "000882 20200123\n",
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      "601002 20200123\n",
      "002227 20200123\n",
      "002527 20200123\n",
      "600208 20200123\n",
      "False ['2019-10-24']\n",
      "600076 20200123\n",
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      "000528 20200123\n",
      "300032 20200123\n",
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      "002703 20200123\n",
      "False ['2019-10-17', '2019-11-22', '2019-12-02']\n",
      "002025 20200123\n",
      "300138 20200123\n",
      "True ['2020-01-07']\n",
      "002936 20200123\n",
      "600581 20200123\n",
      "True ['2019-10-21', '2019-11-22', '2020-01-07']\n",
      "002216 20200123\n",
      "True ['2019-10-28', '2019-11-08', '2019-11-19', '2019-12-20', '2020-01-16']\n",
      "002676 20200123\n",
      "False ['2019-11-29', '2019-12-16']\n",
      "002440 20200123\n",
      "601218 20200123\n",
      "603778 20200123\n",
      "False ['2019-11-13', '2019-12-11']\n",
      "600165 20200123\n",
      "601939 20200123\n",
      "000668 20200123\n",
      "True ['2019-11-19', '2020-01-02', '2020-01-17']\n",
      "000985 20200123\n",
      "False ['2019-10-31', '2019-12-25', '2019-12-30']\n",
      "002083 20200123\n",
      "False ['2019-12-12', '2019-12-30']\n",
      "600967 20200123\n",
      "000963 20200123\n",
      "False ['2019-11-07', '2019-12-16']\n",
      "601212 20200123\n",
      "002361 20200123\n",
      "600400 20200123\n",
      "True ['2020-01-20']\n",
      "000573 20200123\n",
      "300486 20200123\n",
      "True ['2019-11-18', '2019-11-28', '2019-12-16', '2020-01-03', '2020-01-09']\n",
      "603696 20200123\n",
      "600981 20200123\n",
      "False ['2019-10-28']\n",
      "600322 20200123\n",
      "601838 20200123\n",
      "000822 20200123\n",
      "False ['2019-12-10']\n",
      "603728 20200123\n",
      "000922 20200123\n",
      "False ['2019-10-28', '2019-12-16', '2019-12-24']\n",
      "600303 20200123\n",
      "False ['2019-11-07']\n",
      "300276 20200123\n",
      "600182 20200123\n",
      "000888 20200123\n",
      "False ['2019-10-28', '2019-11-04']\n",
      "300215 20200123\n",
      "600293 20200123\n",
      "False ['2019-10-28', '2019-12-03']\n",
      "002647 20200123\n",
      "True ['2019-11-14', '2019-11-21', '2020-01-09']\n",
      "300193 20200123\n",
      "False ['2019-10-25']\n",
      "601019 20200123\n",
      "002536 20200123\n",
      "002093 20200123\n",
      "False ['2019-10-28']\n",
      "002242 20200123\n",
      "True ['2019-10-14', '2019-10-31', '2019-12-09', '2019-12-30', '2020-01-07']\n",
      "300355 20200123\n",
      "False ['2019-12-16', '2019-12-24']\n",
      "000600 20200123\n",
      "False ['2019-12-16']\n",
      "600300 20200123\n",
      "002606 20200123\n",
      "002026 20200123\n",
      "False ['2019-11-26']\n",
      "600257 20200123\n",
      "False ['2019-12-24']\n",
      "002286 20200123\n",
      "False ['2019-11-22', '2019-11-29']\n",
      "002831 20200123\n",
      "False ['2019-10-30', '2019-11-08']\n",
      "603648 20200123\n",
      "002392 20200123\n",
      "False ['2019-10-23', '2019-12-31']\n",
      "603368 20200123\n",
      "False ['2019-11-04']\n",
      "000829 20200123\n",
      "False ['2019-10-23', '2019-10-28', '2019-10-31', '2019-12-25', '2020-01-02']\n",
      "300153 20200123\n",
      "False ['2019-11-01', '2019-12-24']\n",
      "002258 20200123\n",
      "True ['2019-10-14', '2020-01-17']\n",
      "600064 20200123\n",
      "603028 20200123\n",
      "True ['2019-11-14', '2019-12-24', '2020-01-07']\n",
      "600343 20200123\n",
      "002706 20200123\n",
      "False ['2019-10-23']\n",
      "000768 20200123\n",
      "False ['2019-12-16']\n",
      "603980 20200123\n",
      "002355 20200123\n",
      "False ['2019-10-28', '2019-11-04', '2019-12-02', '2019-12-13']\n",
      "600037 20200123\n",
      "000423 20200123\n",
      "True ['2020-01-15', '2020-01-21']\n",
      "300285 20200123\n",
      "True ['2019-11-01', '2020-01-07', '2020-01-20']\n",
      "001696 20200123\n",
      "False ['2019-12-24']\n",
      "600362 20200123\n",
      "False ['2019-12-10', '2019-12-24']\n",
      "002183 20200123\n",
      "False ['2019-10-28']\n",
      "002682 20200123\n",
      "True ['2020-01-20']\n",
      "600588 20200123\n",
      "False ['2019-10-22', '2019-10-28']\n",
      "600452 20200123\n",
      "000819 20200123\n",
      "000811 20200123\n",
      "True ['2020-01-17']\n",
      "600598 20200123\n",
      "000521 20200123\n",
      "False ['2019-11-01']\n",
      "600499 20200123\n",
      "False ['2019-12-24', '2019-12-30']\n",
      "000089 20200123\n",
      "False ['2019-11-13']\n",
      "300140 20200123\n",
      "True ['2019-10-23', '2019-10-28', '2019-11-04', '2020-01-10']\n",
      "603689 20200123\n",
      "002532 20200123\n",
      "601200 20200123\n",
      "002091 20200123\n",
      "True ['2019-12-09', '2019-12-24', '2020-01-13']\n",
      "600308 20200123\n",
      "600338 20200123\n",
      "False ['2019-12-24']\n",
      "601388 20200123\n",
      "False ['2019-12-24']\n",
      "603777 20200123\n",
      "False ['2019-10-18', '2019-12-24', '2020-01-02']\n",
      "002002 20200123\n",
      "True ['2020-01-14']\n",
      "601677 20200123\n",
      "True ['2019-10-17', '2019-10-25', '2019-11-25', '2020-01-14']\n",
      "601595 20200123\n",
      "False ['2019-12-03', '2020-01-02']\n",
      "000019 20200123\n",
      "002132 20200123\n",
      "600894 20200123\n",
      "False ['2019-10-15']\n",
      "002284 20200123\n",
      "False ['2019-10-17', '2019-11-19', '2019-12-19']\n",
      "600054 20200123\n",
      "601975 20200123\n",
      "300732 20200123\n",
      "False ['2019-11-14']\n",
      "000488 20200123\n",
      "000623 20200123\n",
      "600715 20200123\n",
      "True ['2019-10-11', '2019-11-19', '2019-12-11', '2020-01-02', '2020-01-07']\n",
      "601688 20200123\n",
      "601881 20200123\n",
      "002178 20200123\n",
      "False ['2019-10-17', '2019-11-19', '2019-12-03']\n",
      "002390 20200123\n",
      "False ['2019-10-29']\n",
      "600490 20200123\n",
      "True ['2019-11-19', '2019-12-24', '2020-01-14']\n",
      "600063 20200123\n",
      "False ['2019-12-20']\n",
      "600635 20200123\n",
      "000966 20200123\n",
      "False ['2019-10-25']\n",
      "600502 20200123\n",
      "600590 20200123\n",
      "600622 20200123\n",
      "002006 20200123\n",
      "True ['2019-12-25', '2020-01-07']\n",
      "002726 20200123\n",
      "False ['2019-10-21', '2019-10-24', '2019-11-12']\n",
      "300103 20200123\n",
      "False ['2019-11-12']\n",
      "002044 20200123\n",
      "False ['2019-10-28', '2019-11-01', '2019-12-16']\n",
      "600368 20200123\n",
      "601918 20200123\n",
      "False ['2019-10-31']\n",
      "600869 20200123\n",
      "600467 20200123\n",
      "603311 20200123\n",
      "True ['2019-11-19', '2019-11-25', '2020-01-20']\n",
      "603010 20200123\n",
      "False ['2019-11-08', '2019-12-25']\n",
      "002088 20200123\n",
      "False ['2019-12-13']\n",
      "000826 20200123\n",
      "002097 20200123\n",
      "600066 20200123\n",
      "False ['2019-11-12', '2019-11-26']\n",
      "002302 20200123\n",
      "000783 20200123\n",
      "True ['2020-01-10']\n",
      "601222 20200123\n",
      "600418 20200123\n",
      "False ['2019-11-01']\n",
      "002575 20200123\n",
      "False ['2019-11-01']\n",
      "002816 20200123\n",
      "False ['2019-11-01']\n",
      "601319 20200123\n",
      "603169 20200123\n",
      "False ['2019-12-24']\n",
      "600329 20200123\n",
      "False ['2019-11-01']\n",
      "603203 20200123\n",
      "False ['2019-12-25']\n",
      "002264 20200123\n",
      "False ['2019-11-14', '2019-11-25']\n",
      "000407 20200123\n",
      "002033 20200123\n",
      "600109 20200123\n",
      "002165 20200123\n",
      "002208 20200123\n",
      "False ['2019-11-14']\n",
      "300180 20200123\n",
      "002363 20200123\n",
      "True ['2019-12-20', '2020-01-13']\n",
      "000584 20200123\n",
      "False ['2019-12-16']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "002024 20200123\n",
      "False ['2019-10-28']\n",
      "002496 20200123\n",
      "600369 20200123\n",
      "True ['2020-01-20']\n",
      "300387 20200123\n",
      "True ['2019-11-19', '2020-01-22']\n",
      "002489 20200123\n",
      "True ['2020-01-10']\n",
      "002167 20200123\n",
      "False ['2019-12-24']\n",
      "600859 20200123\n",
      "False ['2019-12-06']\n",
      "600191 20200123\n",
      "False ['2019-10-22']\n",
      "300149 20200123\n",
      "True ['2019-10-31', '2019-12-10', '2020-01-20']\n",
      "603008 20200123\n",
      "False ['2019-11-18', '2019-11-26', '2019-12-17']\n",
      "002226 20200123\n",
      "False ['2019-12-16']\n",
      "002393 20200123\n",
      "True ['2019-10-30', '2019-12-16', '2020-01-20']\n",
      "002058 20200123\n",
      "False ['2019-10-29', '2019-11-04']\n",
      "300261 20200123\n",
      "False ['2019-12-16']\n",
      "600097 20200123\n",
      "False ['2019-11-14']\n",
      "002331 20200123\n",
      "False ['2019-10-22']\n",
      "002265 20200123\n",
      "000025 20200123\n",
      "False ['2019-10-18']\n",
      "600970 20200123\n",
      "False ['2019-11-25']\n",
      "002564 20200123\n",
      "600880 20200123\n",
      "False ['2019-10-17', '2019-10-28']\n",
      "300470 20200123\n",
      "False ['2019-10-14']\n",
      "600837 20200123\n",
      "603077 20200123\n",
      "600765 20200123\n",
      "False ['2019-12-31']\n",
      "002150 20200123\n",
      "True ['2019-11-12', '2019-12-05', '2019-12-16', '2019-12-19', '2019-12-26', '2020-01-17']\n",
      "600381 20200123\n",
      "False ['2019-11-29']\n",
      "000913 20200123\n",
      "True ['2020-01-20']\n",
      "600252 20200123\n",
      "False ['2019-12-16']\n",
      "002228 20200123\n",
      "False ['2019-10-17', '2019-10-28']\n",
      "600597 20200123\n",
      "False ['2019-12-30']\n",
      "601111 20200123\n",
      "False ['2019-10-28']\n",
      "000056 20200123\n",
      "600133 20200123\n",
      "False ['2019-10-23']\n",
      "600101 20200123\n",
      "False ['2019-11-19']\n",
      "002538 20200123\n",
      "600495 20200123\n",
      "603021 20200123\n",
      "False ['2019-11-13', '2019-11-19']\n",
      "002399 20200123\n",
      "True ['2019-11-18', '2019-11-26', '2020-01-06', '2020-01-10']\n",
      "000012 20200123\n",
      "False ['2019-10-22']\n",
      "601333 20200123\n",
      "002068 20200123\n",
      "False ['2019-11-25', '2019-11-29']\n",
      "000615 20200123\n",
      "002601 20200123\n",
      "002820 20200123\n",
      "601366 20200123\n",
      "002270 20200123\n",
      "False ['2019-11-27']\n",
      "300151 20200123\n",
      "False ['2019-10-28', '2019-12-06']\n",
      "600901 20200123\n",
      "600125 20200123\n",
      "600438 20200123\n",
      "False ['2019-10-22', '2019-10-25', '2019-11-05', '2019-11-19']\n",
      "002391 20200123\n",
      "600963 20200123\n",
      "002818 20200123\n",
      "False ['2019-10-22']\n",
      "600239 20200123\n",
      "600287 20200123\n",
      "603063 20200123\n",
      "True ['2020-01-07']\n",
      "000731 20200123\n",
      "000889 20200123\n",
      "False ['2019-10-28']\n",
      "000404 20200123\n",
      "000932 20200123\n",
      "True ['2019-11-25', '2020-01-07']\n",
      "603618 20200123\n",
      "600160 20200123\n",
      "True ['2019-10-28', '2019-11-18', '2020-01-22']\n",
      "002659 20200123\n",
      "False ['2019-11-19', '2019-12-16']\n",
      "600558 20200123\n",
      "False ['2019-12-04']\n",
      "603993 20200123\n",
      "True ['2019-11-25', '2019-12-24', '2020-01-14']\n",
      "000516 20200123\n",
      "False ['2019-12-16']\n",
      "600830 20200123\n",
      "False ['2019-10-14']\n",
      "000839 20200123\n",
      "True ['2019-10-28', '2019-11-19', '2020-01-14']\n",
      "002001 20200123\n",
      "False ['2019-10-31', '2019-11-12', '2019-12-02', '2019-12-31']\n",
      "601377 20200123\n",
      "False ['2019-12-25']\n",
      "600315 20200123\n",
      "002395 20200123\n",
      "False ['2019-10-28', '2019-12-09', '2019-12-17', '2020-01-03']\n",
      "600533 20200123\n",
      "600331 20200123\n",
      "False ['2019-10-31', '2019-11-25']\n",
      "600083 20200123\n",
      "True ['2019-10-18', '2019-10-29', '2019-11-13', '2019-12-05', '2019-12-31', '2020-01-13']\n",
      "000055 20200123\n",
      "False ['2019-11-22', '2019-12-03']\n",
      "300284 20200123\n",
      "600157 20200123\n",
      "True ['2019-11-25', '2020-01-07']\n",
      "600071 20200123\n",
      "False ['2019-10-22', '2019-11-04', '2019-12-17', '2019-12-24']\n",
      "300583 20200123\n",
      "False ['2019-12-26']\n",
      "002145 20200123\n",
      "False ['2019-11-21', '2019-11-28']\n",
      "002574 20200123\n",
      "002546 20200123\n",
      "300117 20200123\n",
      "600686 20200123\n",
      "000002 20200123\n",
      "True ['2020-01-22']\n",
      "002471 20200123\n",
      "False ['2019-11-19', '2019-12-05']\n",
      "600458 20200123\n",
      "002327 20200123\n",
      "600123 20200123\n",
      "603377 20200123\n",
      "False ['2019-12-25']\n",
      "600895 20200123\n",
      "002238 20200123\n",
      "True ['2019-11-19', '2020-01-02', '2020-01-14']\n",
      "603030 20200123\n",
      "False ['2019-12-26']\n",
      "000060 20200123\n",
      "False ['2019-12-24']\n",
      "603108 20200123\n",
      "False ['2019-11-06']\n",
      "002378 20200123\n",
      "False ['2019-10-15', '2019-11-20', '2019-12-24']\n",
      "000591 20200123\n",
      "False ['2019-11-25']\n",
      "600710 20200123\n",
      "600637 20200123\n",
      "002034 20200123\n",
      "False ['2019-10-22']\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tushare as ts\n",
    "from six import StringIO\n",
    "from six import BytesIO\n",
    "from funcat import *\n",
    "import re\n",
    "import talib as tb\n",
    "import time\n",
    "from datetime import datetime, date\n",
    "from datetime import timedelta\n",
    "def main_1_1():\n",
    "# 1.1 当下时间\n",
    "    \"\"\"\n",
    "    1)今天时间/开始时间/tushare时间\n",
    "    2）输出时间名字\n",
    "    \"\"\"\n",
    "#今天时间（str 年+月+日）\n",
    "    data_time_end=\"{}\".format(time.localtime( time.time())[0]) +\"{}\".format(time.localtime( time.time())[1])+\"{}\".format(time.localtime( time.time())[2])                                                               #以昨天为基点，需要手动输入\n",
    "#开始时间，不规则data_time_start_1\n",
    "    data_time_start_1=(datetime.now() - timedelta(days=120))\n",
    "#开始时间，规则\n",
    "    data_time_start=data_time_start_1.strftime(\"%Y\")+data_time_start_1.strftime(\"%m\")+data_time_start_1.strftime(\"%d\")\n",
    "#从tushare中寻找开始和结束时间\n",
    "    ts.set_token('ed67d0dc90bea781f7a39a408eeeb423b4166de60ef3d9108b38ed1c')\n",
    "    pro = ts.pro_api()\n",
    "    i_time_day= pro.daily(ts_code='000001.SZ', start_date=data_time_start, end_date=data_time_end)['trade_date'][0]#（时间30天左右）\n",
    "\n",
    "#输出 今天时间名字\n",
    "    data_txt_name=\"{}\".format(time.localtime( time.time())[0])+\"_\"+\"{}\".format(time.localtime( time.time())[1])+\"_\"+\"{}\".format(time.localtime( time.time())[2])+\".txt\"   \n",
    "    \n",
    "    return i_time_day ,data_time_start,data_txt_name\n",
    "#————————\n",
    "def main_1_2(test_name,end_time,start_time):\n",
    "    \"\"\"\n",
    "    1)获取数据\n",
    "    2）数据清洗/选择\n",
    "        1补缺\n",
    "        2去重\n",
    "        3清洗st\n",
    "        4过滤空值\n",
    "        5数据选择 换手率/波动\n",
    "        换手率降序\n",
    "        去新股\n",
    "        去天数不符合的数据\n",
    "    \"\"\"   \n",
    "#1下载数据，保存到本地\n",
    "    df_gp_today=ts.get_today_all()             #读取today实时数据\n",
    "    \n",
    "    #body = get_file('gupiao/上午-2020-1-1.txt')             #读取本地数据     ++(,encoding = \"utf-8\")       \n",
    "    #data=pd.read_csv(BytesIO(body))             #转化\n",
    "    #df_gp_today=pd.DataFrame(data)\n",
    "    #print(df_gp_today)\n",
    "#df_gp_today.info()                         #显示数据类型\n",
    "\n",
    "    csv_data=pd.DataFrame(df_gp_today)\n",
    "    #print(csv_data)\n",
    "    csv_data['code']=csv_data ['code'].astype(str).str.zfill(6)                            #1补缺\n",
    "    csv_data=csv_data.loc[:, ~csv_data.columns.str.contains('^Unnamed')].drop_duplicates() #2去掉Unnamed 并且去重\n",
    "    csv_data=csv_data[~csv_data.name.str.contains('ST')]                                  #3洗掉ST\n",
    "    csv_data=csv_data[~csv_data['open'].isin([0])]                                        #4过滤开盘为0\n",
    "    csv_data=csv_data[csv_data[\"turnoverratio\"]>1]                                      #5.换手率大于2.5\n",
    "    csv_data=csv_data[csv_data[\"changepercent\"]>-5]                                       #5.删掉-3%以上的\n",
    "    csv_data=csv_data[csv_data[\"changepercent\"]<6]  \n",
    "    #csv_data=csv_data[(csv_data[\"amount\"]>100000000)]\n",
    "    #csv_data=csv_data[(csv_data[\"amount\"]>100000000) | (csv_data[\"amount\"]>180000000*csv_data[\"turnoverratio\"]) ]#成交额大于1.5亿，成交额大于1亿\n",
    "    csv_data=csv_data[csv_data[\"trade\"]<35]\n",
    "    csv_data=csv_data.sort_values(by=\"turnoverratio\", ascending=False)         #换手率按降序排列\n",
    "#————————\n",
    "    #去新股\n",
    "    ts.set_token('ed67d0dc90bea781f7a39a408eeeb423b4166de60ef3d9108b38ed1c')\n",
    "    pro = ts.pro_api() \n",
    "    data_lishi_temp = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date').rename(columns={'symbol':'code'})\n",
    "#时间大于开始时间\n",
    "    data_quxingu =data_lishi_temp[data_lishi_temp['list_date']<'{}'.format(start_time)]\n",
    "#数据合并\n",
    "    df_hebing=pd.merge(csv_data,data_quxingu[['code','list_date','ts_code']],how='inner',on='code')\n",
    "#成交量预测\n",
    "    df_hebing=V_predict(data=df_hebing)\n",
    "    df_hebing=df_hebing[(df_hebing[\"trade\"]*df_hebing[\"volume_predict\"]>100000000)]\n",
    "    #print(df_hebing)\n",
    "\n",
    "#————————\n",
    "#得到天数，过滤停牌等数据\n",
    "    end_time='{}'.format(end_time)                                                            \n",
    "    start_time='{}'.format(start_time)                                                             \n",
    "    i_time_day= pro.daily(ts_code='000001.SZ', start_date=start_time, end_date=end_time)['trade_date']\n",
    "    code_list=[]\n",
    "    V_list=[]\n",
    "    MA_20_list=[]\n",
    "    MA_50_list=[]\n",
    "    MA_100_list=[]\n",
    "    llv_50_list=[]\n",
    "    V_count=[]\n",
    "    for i_code in df_hebing['ts_code']: \n",
    "    ##接口限制访问200次/分钟，加一点微小的延时防止被ban\n",
    "        time.sleep(0.301)\n",
    "        \n",
    "        #计算天数 ,除掉停牌等，用天数作为判断量\n",
    "        i_time_day_code= pro.daily(ts_code=i_code, start_date=start_time, end_date=end_time)['trade_date']\n",
    "        if len(i_time_day_code)<=len(i_time_day)-1:\n",
    "            #print(i_code)\n",
    "            continue\n",
    "        today_price = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'trade']\n",
    "        today_high = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'high']\n",
    "        today_open = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'open']\n",
    "        today_low = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'low']\n",
    "        today_settlement = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'settlement']\n",
    "        today_v = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'volume']\n",
    "        today_volume_predict = df_hebing.loc[df_hebing['ts_code'] ==i_code, 'volume_predict']\n",
    "        if len((today_price-today_settlement).values)>1: #float64 2会出错，1才是对的,去掉读取不对的（很重要）\n",
    "            #print(i_code)\n",
    "            continue\n",
    "\n",
    "#————————            \n",
    "#去sh、sz结尾\n",
    "        i_code_temp=i_code.strip('.SH')\n",
    "        i_code_temp=i_code_temp.strip('.SZ')\n",
    "        \n",
    "        print(i_code_temp,end_time)\n",
    "        ture_or_not(code=i_code_temp)\n",
    "\"\"\"    \n",
    "        i_code_temp=i_code.strip('.SH')\n",
    "        i_code_temp=i_code_temp.strip('.SZ')\n",
    "#coda正则化，加结尾\n",
    "        if re.match(r'^(3|0).*',i_code_temp):\n",
    "            i_code_temp=i_code_temp+'.XSHE'\n",
    "        if re.match(r'^6.*',i_code_temp):\n",
    "            i_code_temp=i_code_temp+'.XSHG' \n",
    "        if re.match(r'^9.*',i_code_temp) :\n",
    "            continue \n",
    "#funcat开始\n",
    "        S(i_code_temp)\n",
    "        print()\n",
    "        T(end_time) # \"20190820\"\n",
    "        #print(i_code_temp,end_time)\n",
    "        #print(i_code_temp,CLOSE,HIGH,MA(V,120))\n",
    "        #\n",
    "\n",
    "        TR=MAX(MAX((HIGH-LOW),ABS(REF(CLOSE,1)-HIGH)),ABS(REF(CLOSE,1)-LOW))\n",
    "        ATR=MA(TR,10)\n",
    "        std_=STD(CLOSE, 13)\n",
    "        macd3=MACD(SHORT=12, LONG=26, M=9)\n",
    "        today_settlement = today_settlement.item()\n",
    "        today_v = today_v.item()\n",
    "        today_volume_predict =today_volume_predict.item()\n",
    "        V_choose_0=COUNT(V>min(MA(V,120),MA(V,60)),3)>=2 and max(today_volume_predict,(4*MA(V,5)+today_volume_predict)/5)>1.2*max(MA(V,120),min(MA(V,60),MA(V,30)))and today_volume_predict>V\n",
    "        \n",
    "        #diff,macd=MACD_3ge(SHORT=12, LONG=26, M=9)\n",
    "        #V_choose =(today_volume_predict/MA(V,5)>1.5 or COUNT(V>MA(V,120),3)>=2 or today_volume_predict/MA(V,120)>1 or MA(V,30)>MA(V,120)or MA(V,5)>MA(V,120))\n",
    "        V_choose_0=COUNT(V>min(MA(V,120),MA(V,60)),3)>=2 and max(today_volume_predict,(4*MA(V,5)+today_volume_predict)/5)>1.2*max(MA(V,120),min(MA(V,60),MA(V,30)))and today_volume_predict>V\n",
    "        V_choose_1=COUNT(V[1]>min(MA(V[1],120),MA(V[1],60)),3)>=2 and max(V,MA(V,5))>1.2*max(MA(V,120),min(MA(V,60),MA(V,30)))and V>V[1]\n",
    "        BASI_choose_1=-10<(CLOSE - MA(CLOSE, 50)) / MA(CLOSE, 50) * 100<15 #and (today_price>0.95*max(MA(CLOSE, 100), MA(CLOSE, 50)))\n",
    "        TR_choose_1=COUNT(max(TR,ATR)/CLOSE>0.035,5)>=1 and COUNT(TR/CLOSE>0.06,19)>=2 and COUNT(max(V,MA(V,5))/max(MA(V,120),min(MA(V,60),MA(V,30)))>2,19)>=2\n",
    "        CLOSE_choose_1=today_price>MA(CLOSE,20)#and  LLV(LOW,4)<today_low#H>H[1]and CLOSE>CLOSE[1]\n",
    "        #时间模块=0为及时，=1为盘后\n",
    "        time_day_=time.localtime(time.time())\n",
    "        #print(9<time_day_.tm_hour<15)\n",
    "        if 9<time_day_.tm_hour<15:\n",
    "            V_choose_=V_choose_0\n",
    "        if time_day_.tm_hour<9 or time_day_.tm_hour>15:\n",
    "            V_choose_=V_choose_1\n",
    "        if MA(CLOSE, 100)>MA(CLOSE, 50):\n",
    "            choose_=COUNT(CROSS(CLOSE,0.95*MA(CLOSE, 100)),4)>=1\n",
    "        if MA(CLOSE, 100)<=MA(CLOSE, 50):\n",
    "            choose_=COUNT(CROSS(CLOSE,MA(CLOSE, 50)),2)>=1        \n",
    "        \n",
    "        #if  V_choose_0 and BASI_choose_1 and TR_choose_1 and CLOSE_choose_1 and choose_:\n",
    "        \n",
    "        \n",
    "        \n",
    "                #1.RSI\n",
    "        r1,r2,r3=RSI(N1=6, N2=12, N3=24)\n",
    "        rsi_a=max((today_price-today_settlement).item(),0)\n",
    "        rsi_b=abs((today_price-today_settlement).item())\n",
    "        rsi_a1=5*SMA(MAX(CLOSE - REF(CLOSE, 1), 0), 6, 1)\n",
    "        rsi_b1=5*SMA(ABS(CLOSE - REF(CLOSE, 1)), 6, 1)\n",
    "        #RSI_today_a=(MAX((C-today_price)*-1,0)+5*SMA(MAX(C - REF(C, 1), 0), 6, 1))/6\n",
    "        #RSI_today_b=(ABS(C-today_price)+5*SMA(ABS(CLOSE - REF(C, 1)), 6, 1))/6\n",
    "        #RSI_today=RSI_today_a/RSI_today_b* 100\n",
    "        RSI_today=(rsi_a+rsi_a1)/(rsi_b+rsi_b1)* 100\n",
    "        RX=(r1>52 or r1[1]>52 or r1[2]>52)\n",
    "        ##print(i_code,\"RX_昨天\",RX)\n",
    "        \n",
    "        #2.tr,atr=ATR(M1=10)\n",
    "        TR=MAX(MAX((HIGH-LOW),ABS(REF(CLOSE,1)-HIGH)),ABS(REF(CLOSE,1)-LOW))\n",
    "        ATR=SUM(TR,10)/10\n",
    "        #\n",
    "        tr_today=MAX(MAX((today_high-today_low).item(),ABS(CLOSE-today_high.item())),ABS(CLOSE-today_low.item()))\n",
    "        atr_today=(SUM(TR,9)+tr_today)/10\n",
    "        #\n",
    "        atr_bo=MAX(tr_today,atr_today)/today_price\n",
    "        atr_b=atr_bo>0.04\n",
    "\n",
    "        TRX=max(TR,ATR)/CLOSE>0.035 or max(TR[1],ATR[1])/CLOSE[1]>0.035 or max(TR[2],ATR[2])/CLOSE[2]>0.035\n",
    "        ##print(\"TR_昨天\",TRX)\n",
    "\n",
    "\n",
    "        #4.std \n",
    "        std_yestday_list=[]\n",
    "        for i in range(0,13):\n",
    "            std_yestday_i=float(str(CLOSE[i]))\n",
    "            std_yestday_list.append(std_yestday_i)\n",
    "            if len(std_yestday_list)==13:\n",
    "                #print(std_yestday_list)\n",
    "                std_today_list=std_yestday_list[0:12]\n",
    "                std_today_list.append(today_price.item())\n",
    "                std_yestday_1_list=std_yestday_list[1:13]\n",
    "                std_yestday_1_list.append(float(str(CLOSE[13])))\n",
    "                std_yestday_1=np.std(std_yestday_1_list)\n",
    "                #print(std_yestday1)\n",
    "                std_yestday_2_list=std_yestday_list[2:13]\n",
    "                std_yestday_2_list.append(float(str(CLOSE[13])))\n",
    "                std_yestday_2_list.append(float(str(CLOSE[14])))\n",
    "                std_yestday_2=np.std(std_yestday_2_list)\n",
    "                #print(std_yestday_2_list)\n",
    "                #print(std_yestday_list)\n",
    "                #print(std_today_list)\n",
    "                #print(\"OK\")\n",
    "\n",
    "                #std_yestday_1=np.std(std_yestday_1_list)\n",
    "\n",
    "                std_yestday=np.std(std_yestday_list)\n",
    "                std_today=np.std(std_today_list)\n",
    "                #print(std_yestday_1)\n",
    "                #print(std_yestday,std_today)\n",
    "        #STDX=std_yestday_2<std_yestday_1 or std_yestday_1<std_yestday or std_yestday <std_today\n",
    "        STDX=COUNT(STD(CLOSE, 13)>STD(CLOSE, 13)[1],3)>=1\n",
    "        ##print(\"STD_昨天\",STDX)\n",
    "\n",
    "        HV_1=(COUNT(H>REF(HHV(H,10),1),3)>=1 or today_high.item()>HHV(H,10))\n",
    "\n",
    "            \n",
    "        #if b2<6 and (V>min( V_30,V_60) or 5*V/SUM(V,5)>1.5) and (V_t or V_t1) and TRX and COUNT(TR/CLOSE>0.06,19)>=2 and RX  and HV_1 and STDX and 0.97<CLOSE/CLOSE[1]<1.075 and today_price.item()>(H+L)/2 and (today_price.item()-today_open.item())>(today_high.item()-today_low.item())/3:\n",
    "                  \n",
    "        \n",
    "\n",
    "        #if (V>min( V_30,V_60) or 5*V/SUM(V,5)>1.5) and (V_t or V_t1) and TRX and COUNT(TR/CLOSE>0.06,19)>=2 and RX  and HV_1 and STDX and 0.97<CLOSE/CLOSE[1]<1.075 and today_price.item()>(H+L)/2 and (today_price.item()-today_open.item())>(today_high.item()-today_low.item())/3:\n",
    "        #if 0.85*max(MA(CLOSE,50),MA(CLOSE,100))<CLOSE<1.15*max(MA(CLOSE,50),MA(CLOSE,100)) and today_volume_predict>MA(V,5) and today_volume_predict>2*max(MA(V,120),min(MA(V,60),MA(V,30))):\n",
    "        plan_1=COUNT(V>min(MA(V,120),MA(V,60)),3)>=2 and today_volume_predict>1.5*max(MA(V,120),min(MA(V,60),MA(V,30))) and COUNT(max(TR,ATR)/CLOSE>0.035,3)>=1 and today_price.item()>MA(CLOSE,20) and today_price.item()/LLV(CLOSE,50)<1.2\n",
    "        plan_2=COUNT(COUNT(V>min(MA(V,120),MA(V,60)),3)>=2 and V>1.5*max(MA(V,120),min(MA(V,60),MA(V,30))) and COUNT(max(TR,ATR)/CLOSE>0.035,3)>=1 and CLOSE>MA(CLOSE,20) and CLOSE/LLV(CLOSE,50)<1.2,3)>=1\n",
    "        if plan_1:\n",
    "        #HV_1 and V_choose_0 and COUNT(CROSS(CLOSE,MA(CLOSE,50)),7)>=1and STDX\n",
    "        #if COUNT(V>min(MA(V,120),MA(V,60)),3)>=2 and COUNT(max(TR,ATR)/CLOSE>0.035,3)>=1 and COUNT(std_>std_[1],3)>=1and COUNT(CROSS(CLOSE,MA(CLOSE,50)),4)>=1:\n",
    "        #if  V_choose and CLOSE>MA(CLOSE,20)  and today_price/LLV(CLOSE,50)<1.3 and TRX and COUNT(TR/CLOSE>0.06,19)>=2:\n",
    "            #print(i_code_temp,\"MA(V,120)\",MA(V,120),\"today_v\",today_v,\"today_volume_predict\",today_volume_predict)\n",
    "            code_list.append(i_code_temp[:6])\n",
    "            print(i_code_temp[:6],V[1],min(MA(V[1],120),MA(V[1],60)))\n",
    "            V_list.append(float(str(MA(V,120)[0])))\n",
    "            MA_20_list.append(float(str(MA(CLOSE,20)[0])))\n",
    "            MA_50_list.append(float(str(MA(CLOSE,50)[0])))\n",
    "            MA_100_list.append(float(str(MA(CLOSE,100)[0])))\n",
    "            llv_50_list.append(float(str(LLV(CLOSE,50))))\n",
    "            V_count.append(float(str(max(V,MA(V,5))/max(MA(V,120),min(MA(V,60),MA(V,30))))))\n",
    "    #V_list=np.array(V_list,dtype=\"int32\")\n",
    "    print(code_list,V_list)\n",
    "    #zidian={\"code\":code_list,\"MA_120\": V_list}#将列表a，b转换成字典\n",
    "    #data_collect_v=pd.DataFrame(zidian)#将字典转换成为数据框\n",
    "    #print(data_collect_v)\n",
    "    #df_hebing=df_hebing.insert(0,'MA_120',V_list)\n",
    "    df_hebing=df_hebing[df_hebing['code'].isin(code_list)]\n",
    "    df_hebing[\"MA_120\"]=V_list\n",
    "    df_hebing[\"MA_20\"]=MA_20_list\n",
    "    df_hebing[\"MA_50\"]=MA_50_list\n",
    "    df_hebing[\"MA_100\"]=MA_100_list\n",
    "    df_hebing[\"llv_50\"]=llv_50_list\n",
    "    df_hebing[\"V_count\"]=V_count\n",
    "    df_hebing=df_hebing.sort_values(by=\"V_count\", ascending=False)\n",
    "    \n",
    "    print(df_hebing)\n",
    "    df_hebing.to_csv(\"df_hebing.txt\",encoding = \"utf-8\")         #对数据进行本地保存\n",
    "\"\"\"\n",
    " #___________\n",
    "def V_predict(data):\n",
    "    \"\"\"\n",
    "    当日成交量预测\n",
    "    1）根据时间计算今日时长\n",
    "    2）根据V*4/时长\n",
    "    3）\n",
    "    \"\"\"\n",
    "    time_day_=time.localtime(time.time())\n",
    "    if time_day_.tm_hour-9.5+time_day_.tm_min/60<=0:\n",
    "        time_day_time=4\n",
    "    if 0<time_day_.tm_hour-9.5+time_day_.tm_min/60<=2:\n",
    "        time_day_time=time_day_.tm_hour-9.5+time_day_.tm_min/60\n",
    "    if 2<time_day_.tm_hour-9.5+time_day_.tm_min/60<=3.5:\n",
    "        time_day_time=2  \n",
    "    if 3.5<time_day_.tm_hour-9.5+time_day_.tm_min/60<=5.5:\n",
    "        time_day_time=time_day_.tm_hour-11+time_day_.tm_min/60\n",
    "    if 5.5<time_day_.tm_hour-9.5+time_day_.tm_min/60:\n",
    "        time_day_time=4    \n",
    "    #print(time_day_time)\n",
    "    time_day_timepass=4/time_day_time\n",
    "    #print(time_day_timepass)\n",
    "    data[\"volume_predict\"]=data[\"volume\"]*time_day_timepass\n",
    "    #print(csv_data)\n",
    "    return data\n",
    "def ture_or_not(code):\n",
    "    #显示 \n",
    "    code = code\n",
    "    start_data = '2019-10-05'\n",
    "    end_data = '2020-01-23'\n",
    "    hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "    hist_data=hist_data[::-1]\n",
    "    clop=(hist_data.close/hist_data.close.shift(1)-1)*100\n",
    "    Shape=[0,0,0]\n",
    "    lag1clop=clop.shift(1)\n",
    "    lag2clop=clop.shift(2)\n",
    "    for i in range(3,len(clop)):\n",
    "        if all([lag2clop[i]<0,abs(lag1clop[i])<3,clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5)]):\n",
    "            Shape.append(1)\n",
    "            \n",
    "        else:\n",
    "            Shape.append(0)\n",
    "    #print(Shape)\n",
    "    Shape_list=[]\n",
    "    for i in range(len(clop)):\n",
    "        #print(i,hist_data.index[i],clop[i])\n",
    "        #if i==239:\n",
    "            #print(\"OK\",lag2clop[i]<0,abs(lag1clop[i]),clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5))\n",
    "        if all([Shape[i]==1]):\n",
    "            #print(hist_data.index[i])\n",
    "            Shape_list.append(hist_data.index[i])\n",
    "    if len(Shape_list)>0:\n",
    "        print(Shape_list[len(Shape_list)-1]>'2020-01-05',Shape_list)\n",
    "#————————————\n",
    "now = datetime.now()\n",
    "#print(now.strftime('%Y-%m-%d %h:%m:%s'))\n",
    "data_time_end,data_time_start,data_txt_name=main_1_1() #开始时间/结束时间/时间名字\n",
    "csv_data=main_1_2(data_txt_name,data_time_end,data_time_start)                       #获取数据，清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2019-10-08         NaN\n",
      "2019-10-09         NaN\n",
      "2019-10-10    1.396648\n",
      "2019-10-11   -0.459137\n",
      "2019-10-14   -0.276753\n",
      "2019-10-15    1.942646\n",
      "2019-10-16   -1.814882\n",
      "2019-10-17   -2.218115\n",
      "2019-10-18   -0.661626\n",
      "2019-10-21   -2.378687\n",
      "2019-10-22   -0.682261\n",
      "2019-10-23    4.808636\n",
      "2019-10-24    0.468165\n",
      "2019-10-25    1.118360\n",
      "2019-10-28    0.276498\n",
      "2019-10-29    2.665441\n",
      "2019-10-30   -1.880036\n",
      "2019-10-31    0.000000\n",
      "2019-11-01   -0.821168\n",
      "2019-11-04   -0.551978\n",
      "2019-11-05    3.792784\n",
      "2019-11-06    4.188948\n",
      "2019-11-07    0.342173\n",
      "2019-11-08   -0.341006\n",
      "2019-11-11    4.106074\n",
      "2019-11-12    4.272802\n",
      "2019-11-13    3.152088\n",
      "2019-11-14   -3.132162\n",
      "2019-11-15   -4.100946\n",
      "2019-11-18   -4.605263\n",
      "                ...   \n",
      "2019-12-12   -0.452080\n",
      "2019-12-13    0.000000\n",
      "2019-12-16   -1.544051\n",
      "2019-12-17    2.675277\n",
      "2019-12-18    3.773585\n",
      "2019-12-19   -0.952381\n",
      "2019-12-20    4.195804\n",
      "2019-12-23   -3.104027\n",
      "2019-12-24   -1.818182\n",
      "2019-12-25    3.703704\n",
      "2019-12-26    0.085034\n",
      "2019-12-27    1.104503\n",
      "2019-12-30   -3.109244\n",
      "2019-12-31   -0.867303\n",
      "2020-01-02    0.262467\n",
      "2020-01-03    2.181501\n",
      "2020-01-06    0.683177\n",
      "2020-01-07    1.357082\n",
      "2020-01-08    0.920502\n",
      "2020-01-09   -1.575456\n",
      "2020-01-10    1.432182\n",
      "2020-01-13   -1.910299\n",
      "2020-01-14    0.677392\n",
      "2020-01-15   -1.513877\n",
      "2020-01-16   -0.768574\n",
      "2020-01-17    0.000000\n",
      "2020-01-20   -2.495697\n",
      "2020-01-21    2.206531\n",
      "2020-01-22    0.863558\n",
      "2020-01-23   -2.311644\n",
      "Name: close, Length: 77, dtype: float64\n",
      "['2019-10-22', '2019-11-04', '2019-12-17', '2019-12-24']\n",
      "True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    "\n",
    "def ture_or_not():\n",
    "    #显示 \n",
    "    code = '600071' \n",
    "    start_data = '2019-10-05'\n",
    "    end_data = '2020-01-23'\n",
    "    hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "    hist_data=hist_data[::-1]\n",
    "    clop=(hist_data.close/hist_data.close.shift(1)-1)*100\n",
    "    Shape=[0,0,0]\n",
    "    lag1clop=clop.shift(1)\n",
    "    print(lag1clop)\n",
    "    lag2clop=clop.shift(2)\n",
    "    for i in range(3,len(clop)):\n",
    "        if all([lag2clop[i]<0,abs(lag1clop[i])<3,clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5)]):\n",
    "            Shape.append(1)\n",
    "        else:\n",
    "            Shape.append(0)\n",
    "    #print(Shape)\n",
    "    Shape_list=[]\n",
    "    for i in range(len(clop)):\n",
    "        #print(i,hist_data.index[i],clop[i])\n",
    "        #if i==239:\n",
    "            #print(\"OK\",lag2clop[i]<0,abs(lag1clop[i]),clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5))\n",
    "        if all([Shape[i]==1]):\n",
    "            #print(hist_data.index[i])\n",
    "            Shape_list.append(hist_data.index[i])\n",
    "    print(Shape_list)\n",
    "    print(Shape_list[len(Shape_list)-1]<'2020-01-05')\n",
    "    #Shape.index(1)\n",
    "    print()\n",
    "#candleplot\n",
    "ture_or_not()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603238\n"
     ]
    }
   ],
   "source": [
    "a='603238.exe'\n",
    "print(a.split(\".\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    " \n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    " \n",
    "#显示   \n",
    "code = '002945' \n",
    "start_data = '2019-10-05'\n",
    "end_data = '2020-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "a=hist_data[::-1]\n",
    "#print(a)\n",
    "#print(a.ma20[-5],a.volume[-5])\n",
    "#print(a.loc['2020-01-17','close']>a.loc['2020-01-17','ma20'])\n",
    "#print(a.loc[a.index[-5],'open'])\n",
    "\n",
    "\n",
    "clop=(a.close/a.close.shift(1)-1)*100\n",
    "a['clop']=clop\n",
    "a['lag1clop']=clop.shift(1)\n",
    "a['lag2clop']=clop.shift(2)\n",
    "b=a[(a.lag2clop<=0)&(abs(a.lag1clop)<3)&(a.clop>2.5)&(a.clop>abs(a.lag2clop*0.5))].index.tolist() \n",
    "a[\"xx\"]=a.index\n",
    "a[\"xx\"]=a.xx.shift(-1)\n",
    "print(a,b)\n",
    "print(a.iloc[0])\n",
    "if re.match(r'^(3|0).*',code):\n",
    "    i_code_temp=code+'.XSHE'\n",
    "if re.match(r'^6.*',code):\n",
    "    i_code_temp=code+'.XSHG' \n",
    "        \n",
    "        #print(b[len(b)-1])\n",
    "S(i_code_temp)\n",
    "T(b[len(b)-1])\n",
    "\n",
    "if V>MA(V,120) and '2020-01-18'<b[len(b)-1]<'2020-01-23':\n",
    "    xxx=a.loc[b[len(b)-1],\"xx\"]\n",
    "    print(xxx)\n",
    "    close_next=a.loc[xxx,\"close\"]\n",
    "    print(i_code_temp,b[len(b)-1],b[len(b)-1]>'2020-01-20',close_next>CLOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    },
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 2 column 1 (char 1)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mJSONDecodeError\u001B[0m                           Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-1-59ca4e087719>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      4\u001B[0m \u001B[0ma\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mresponse\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mread\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'utf-8'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtype\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ma\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 6\u001B[1;33m \u001B[0mjson\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mloads\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ma\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\json\\__init__.py\u001B[0m in \u001B[0;36mloads\u001B[1;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001B[0m\n\u001B[0;32m    352\u001B[0m             \u001B[0mparse_int\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mparse_float\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m \u001B[1;32mand\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    353\u001B[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001B[1;32m--> 354\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0m_default_decoder\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    355\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mcls\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    356\u001B[0m         \u001B[0mcls\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mJSONDecoder\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\json\\decoder.py\u001B[0m in \u001B[0;36mdecode\u001B[1;34m(self, s, _w)\u001B[0m\n\u001B[0;32m    337\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    338\u001B[0m         \"\"\"\n\u001B[1;32m--> 339\u001B[1;33m         \u001B[0mobj\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mend\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mraw_decode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0midx\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0m_w\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    340\u001B[0m         \u001B[0mend\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_w\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mend\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    341\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mend\u001B[0m \u001B[1;33m!=\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda5.0\\lib\\json\\decoder.py\u001B[0m in \u001B[0;36mraw_decode\u001B[1;34m(self, s, idx)\u001B[0m\n\u001B[0;32m    355\u001B[0m             \u001B[0mobj\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mend\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mscan_once\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0midx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    356\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0mStopIteration\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 357\u001B[1;33m             \u001B[1;32mraise\u001B[0m \u001B[0mJSONDecodeError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"Expecting value\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mvalue\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    358\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mobj\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mend\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mJSONDecodeError\u001B[0m: Expecting value: line 2 column 1 (char 1)"
     ]
    }
   ],
   "source": [
    "import urllib.request\n",
    "import json\n",
    "response=urllib.request.urlopen(\"http://sta.quchaogu.com/dxw/js/staringPlateIndex.js?v=1.1\")\n",
    "a=response.read().decode('utf-8')\n",
    "print(type(a))\n",
    "json.loads(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wytMIO0tOz0i+jwIvj9efBgzE6wvTyhef/yLgfTMsN6b6Z8sZ+wl9jGcTdnQ+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27aff38f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "2020-10-20\n",
      "2020-10-26\n",
      "2020-11-02\n",
      "2020-11-05\n",
      "2020-11-20\n",
      "2020-12-21\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    " \n",
    "#显示   \n",
    "code = '300750' \n",
    "start_data = '2020-10-05'\n",
    "end_data = '2021-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "#print(hist_data)\n",
    "#print(hist_data.close-hist_data.open)\n",
    "  \n",
    "data_list = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    "#print(data_list) \n",
    "fig, ax = plt.subplots()\n",
    "fig.subplots_adjust(bottom=0.2)\n",
    " \n",
    "mpf.candlestick_ohlc(ax, data_list, width=0.7, colorup='r', colordown='green', alpha =.4)\n",
    "plt.grid()\n",
    "ax.xaxis_date()\n",
    "ax.autoscale_view()\n",
    "plt.setp(plt.gca().get_xticklabels(),rotation=30)\n",
    "plt.show()\n",
    "\n",
    "hist_data=hist_data[::-1]\n",
    "#print(hist_data.close)\n",
    "#print(hist_data.close.shift(1))\n",
    "clop=(hist_data.close/hist_data.close.shift(1)-1)*100\n",
    "#print(clop)\n",
    "Shape=[0,0,0]\n",
    "lag1clop=clop.shift(1)\n",
    "lag2clop=clop.shift(2)\n",
    "#print(lag2clop)\n",
    "for i in range(3,len(clop)):\n",
    "    #print(i)\n",
    "    if all([lag2clop[i]<0,abs(lag1clop[i])<3,clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5)]):\n",
    "        Shape.append(1)\n",
    "    else:\n",
    "        Shape.append(0)\n",
    "print(Shape)\n",
    "Shape_list=[]\n",
    "for i in range(len(clop)):\n",
    "    #print(i,hist_data.index[i],clop[i])\n",
    "    #if i==239:\n",
    "        #print(\"OK\",lag2clop[i]<0,abs(lag1clop[i]),clop[i]>3,abs(clop[i])>abs(lag2clop[i]*0.5))\n",
    "    if all([Shape[i]==1]):\n",
    "        print(hist_data.index[i])\n",
    "        Shape_list.append(hist_data.index[i])\n",
    "if len(Shape_list)>0:\n",
    "    print(Shape_list[len(Shape_list)-1]<'2021-01-05')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 75 entries, 2021-01-22 to 2020-10-09\n",
      "Data columns (total 15 columns):\n",
      "open            75 non-null float64\n",
      "high            75 non-null float64\n",
      "close           75 non-null float64\n",
      "low             75 non-null float64\n",
      "volume          75 non-null float64\n",
      "price_change    75 non-null float64\n",
      "p_change        75 non-null float64\n",
      "ma5             75 non-null float64\n",
      "ma10            75 non-null float64\n",
      "ma20            75 non-null float64\n",
      "v_ma5           75 non-null float64\n",
      "v_ma10          75 non-null float64\n",
      "v_ma20          75 non-null float64\n",
      "turnover        75 non-null float64\n",
      "时间              75 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 9.4+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 75 entries, 2021-01-22 to 2020-10-09\n",
      "Data columns (total 15 columns):\n",
      "open            75 non-null float64\n",
      "high            75 non-null float64\n",
      "close           75 non-null float64\n",
      "low             75 non-null float64\n",
      "volume          75 non-null float64\n",
      "price_change    75 non-null float64\n",
      "p_change        75 non-null float64\n",
      "ma5             75 non-null float64\n",
      "ma10            75 non-null float64\n",
      "ma20            75 non-null float64\n",
      "v_ma5           75 non-null float64\n",
      "v_ma10          75 non-null float64\n",
      "v_ma20          75 non-null float64\n",
      "turnover        75 non-null float64\n",
      "时间              75 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 9.4+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x27a83f9dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    "     \n",
    "code = '300750' \n",
    "start_data = '2020-10-05'\n",
    "end_data = '2021-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "hist_data['时间']=pd.to_datetime(hist_data.index,format=\"%Y/%m/%d\")\n",
    "print(hist_data.info())\n",
    "#hist_data['时间']=hist_data['时间'].values\n",
    "#apply(lambda x:dates.date2num(x)*1440)\n",
    "print(hist_data.info())\n",
    "data_list = []\n",
    "data_list_t1 = []\n",
    "data_list_t2 = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    v = row[4:5]\n",
    "    #print(v)\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    "    datas_t1 = (t)\n",
    "    data_list_t1.append(datas_t1)\n",
    "    datas_t2 = (v)\n",
    "    data_list_t2.append(datas_t2) \n",
    "    \n",
    "ax1 =plt.subplot2grid((4,4),(0,0),rowspan=3,colspan=4)\n",
    "mpf.candlestick_ohlc(ax1, data_list, width=0.7, colorup='r', colordown='green', alpha =.4)\n",
    "plt.grid()\n",
    "ax2 =plt.subplot2grid((4,4),(3,0),rowspan=1,colspan=4)\n",
    "#print(data_list_t)\n",
    "#ax2.bar(data_list_t1,data_list_t2,width=0.7)\n",
    "ax2.bar(hist_data['时间'].map(date2num),hist_data['volume'],width=0.7)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 75 entries, 2021-01-22 to 2020-10-09\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   open          75 non-null     float64       \n",
      " 1   high          75 non-null     float64       \n",
      " 2   close         75 non-null     float64       \n",
      " 3   low           75 non-null     float64       \n",
      " 4   volume        75 non-null     float64       \n",
      " 5   price_change  75 non-null     float64       \n",
      " 6   p_change      75 non-null     float64       \n",
      " 7   ma5           75 non-null     float64       \n",
      " 8   ma10          75 non-null     float64       \n",
      " 9   ma20          75 non-null     float64       \n",
      " 10  v_ma5         75 non-null     float64       \n",
      " 11  v_ma10        75 non-null     float64       \n",
      " 12  v_ma20        75 non-null     float64       \n",
      " 13  turnover      75 non-null     float64       \n",
      " 14  时间            75 non-null     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 9.4+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 75 entries, 2021-01-22 to 2020-10-09\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   open          75 non-null     float64       \n",
      " 1   high          75 non-null     float64       \n",
      " 2   close         75 non-null     float64       \n",
      " 3   low           75 non-null     float64       \n",
      " 4   volume        75 non-null     float64       \n",
      " 5   price_change  75 non-null     float64       \n",
      " 6   p_change      75 non-null     float64       \n",
      " 7   ma5           75 non-null     float64       \n",
      " 8   ma10          75 non-null     float64       \n",
      " 9   ma20          75 non-null     float64       \n",
      " 10  v_ma5         75 non-null     float64       \n",
      " 11  v_ma10        75 non-null     float64       \n",
      " 12  v_ma20        75 non-null     float64       \n",
      " 13  turnover      75 non-null     float64       \n",
      " 14  时间            75 non-null     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 9.4+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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g4OgAhRmFk24Lvv/o/qa/X8favj16rF/Pb+w7zt6BvYO/dxjs+yuXYcOGEWvA2yvbTzxE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\npAQlIiK5pAQlIiK59P8Bge5rJEtC9IEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f661a35ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g4OgAhRmFk24Lvv/o/qa/X8favj16rF/Pb+w7zt6BvYO/dxjs+yuXYcOGEWvA2yvbTzxE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\npAQlIiK5pAQlIiK59P8Bge5rJEtC9IEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6618d7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6618d7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "Created on Sun May 20 13:44:23 2018\n",
    "@author: M\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import tushare as ts\n",
    "from matplotlib.pylab import date2num\n",
    "import datetime\n",
    "#import re,urllib2,time,csv,datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.finance as mpf\n",
    "import matplotlib.dates as mpd\n",
    "     \n",
    "code = '300750' \n",
    "start_data = '2020-10-05'\n",
    "end_data = '2021-01-23'\n",
    "hist_data = ts.get_hist_data(code, start=start_data, end=end_data)\n",
    "hist_data['时间']=pd.to_datetime(hist_data.index,format=\"%Y/%m/%d\")\n",
    "print(hist_data.info())\n",
    "#hist_data['时间']=hist_data['时间'].values\n",
    "#apply(lambda x:dates.date2num(x)*1440)\n",
    "print(hist_data.info())\n",
    "data_list = []\n",
    "data_list_t1 = []\n",
    "data_list_t2 = []\n",
    "for dates,row in hist_data.iterrows():\n",
    "    date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')\n",
    "    t = date2num(date_time)\n",
    "    open, high, close, low = row[:4]\n",
    "    v = row[4:5]\n",
    "    #print(v)\n",
    "    datas = (t, open, high, low, close)   #tushare里的数据顺序为open,high,close,low注意\n",
    "    data_list.append(datas)\n",
    "    datas_t1 = (t)\n",
    "    data_list_t1.append(datas_t1)\n",
    "    datas_t2 = (v)\n",
    "    data_list_t2.append(datas_t2) \n",
    "    \n",
    "plt.close('all')\n",
    "fig=plt.figure()\n",
    "    \n",
    "ax1 =plt.subplot2grid((4,4),(0,0),rowspan=3,colspan=4)\n",
    "mpf.candlestick_ohlc(ax1, data_list, width=0.7, colorup='r', colordown='green', alpha =.4)\n",
    "plt.grid()\n",
    "ax2 =plt.subplot2grid((4,4),(3,0),rowspan=1,colspan=4)\n",
    "#print(data_list_t)\n",
    "#ax2.bar(data_list_t1,data_list_t2,width=0.7)\n",
    "ax2.bar(hist_data['时间'].map(date2num),hist_data['volume'],width=0.7)\n",
    "plt.grid(True)\n",
    "plt.tight_layout() \n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "import talib\n",
    "\n",
    "\n",
    "\n",
    "# close = np.asarray(data[\"close\"].values)\n",
    "ma5 = talib.MA(hist_data.open[1:10],timeperiod=5)\n",
    "print(ma5)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}